1
|
Kwon H, Greenberg M, Josephson CB, Lee J. Measuring the prediction difficulty of individual cases in a dataset using machine learning. Sci Rep 2024; 14:10474. [PMID: 38714895 PMCID: PMC11076552 DOI: 10.1038/s41598-024-61284-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/03/2024] [Indexed: 05/12/2024] Open
Abstract
Different levels of prediction difficulty are one of the key factors that researchers encounter when applying machine learning to data. Although previous studies have introduced various metrics for assessing the prediction difficulty of individual cases, these metrics require specific dataset preconditions. In this paper, we propose three novel metrics for measuring the prediction difficulty of individual cases using fully-connected feedforward neural networks. The first metric is based on the complexity of the neural network needed to make a correct prediction. The second metric employs a pair of neural networks: one makes a prediction for a given case, and the other predicts whether the prediction made by the first model is likely to be correct. The third metric assesses the variability of the neural network's predictions. We investigated these metrics using a variety of datasets, visualized their values, and compared them to fifteen existing metrics from the literature. The results demonstrate that the proposed case difficulty metrics were better able to differentiate various levels of difficulty than most of the existing metrics and show constant effectiveness across diverse datasets. We expect our metrics will provide researchers with a new perspective on understanding their datasets and applying machine learning in various fields.
Collapse
Affiliation(s)
- Hyunjin Kwon
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada
- Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Alberta, Canada
| | - Matthew Greenberg
- Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, Alberta, Canada
| | - Colin Bruce Josephson
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Joon Lee
- Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Alberta, Canada.
- Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
- Department of Preventive Medicine, School of Medicine, Kyung Hee University, Seoul, South Korea.
| |
Collapse
|
2
|
Chakraborty J, Midya A, Kurland BF, Welch ML, Gonen M, Moskowitz CS, Simpson AL. Use of Response Permutation to Measure an Imaging Dataset's Susceptibility to Overfitting by Selected Standard Analysis Pipelines. Acad Radiol 2024:S1076-6332(24)00097-7. [PMID: 38614825 DOI: 10.1016/j.acra.2024.02.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 04/15/2024]
Abstract
RATIONALE AND OBJECTIVES This study demonstrates a method for quantifying the impact of overfitting on the receiving operator characteristic curve (AUC) when using standard analysis pipelines to develop imaging biomarkers. We illustrate the approach using two publicly available repositories of radiology and pathology images for breast cancer diagnosis. MATERIALS AND METHODS For each dataset, we permuted the outcome (cancer diagnosis) values to eliminate any true association between imaging features and outcome. Seven types of classification models (logistic regression, linear discriminant analysis, Naïve Bayes, linear support vector machines, nonlinear support vector machine, random forest, and multi-layer perceptron) were fitted to each scrambled dataset and evaluated by each of four techniques (all data, hold-out, 10-fold cross-validation, and bootstrapping). After repeating this process for a total of 50 outcome permutations, we averaged the resulting AUCs. Any increase over a null AUC of 0.5 can be attributed to overfitting. RESULTS Applying this approach and varying sample size and the number of imaging features, we found that failing to control for overfitting could result in near-perfect prediction (AUC near 1.0). Cross-validation offered greater protection against overfitting than the other evaluation techniques, and for most classification algorithms a sample size of at least 200 was required to assess as few as 10 features with less than 0.05 AUC inflation attributable to overfitting. CONCLUSION This approach could be applied to any curated dataset to suggest the number of features and analysis approaches to limit overfitting.
Collapse
Affiliation(s)
- Jayasree Chakraborty
- Department of Surgery, Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Abhishek Midya
- Department of Surgery, Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | | | - Mattea L Welch
- Princess Margaret Data Science Program, University Health Network, Toronto, Ontario, Canada
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Amber L Simpson
- School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Ontario, Canada.
| |
Collapse
|
3
|
Zaylaa AJ, Kourtian S. Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:2312. [PMID: 38610522 PMCID: PMC11014206 DOI: 10.3390/s24072312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/24/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024]
Abstract
Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.
Collapse
Affiliation(s)
- Amira J. Zaylaa
- Biomedical Engineering Program, Electrical and Computer Engineering Department, Faculty of Engineering, Beirut Arab University, Debbieh P.O. Box 11-5020, Lebanon
| | - Sylva Kourtian
- Centre de Recherche du Centre Hospitalier, l’Université de Montréal, Montréal, QC H2X 0A9, Canada;
| |
Collapse
|
4
|
Hidayat T, Ahmad A, Ngo HC. Non-redundant implicational base of formal context with constraints using SAT. PeerJ Comput Sci 2024; 10:e1806. [PMID: 38435549 PMCID: PMC10909189 DOI: 10.7717/peerj-cs.1806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 12/18/2023] [Indexed: 03/05/2024]
Abstract
An implicational base is knowledge extracted from a formal context. The implicational base of a formal context consists of attribute implications which are sound, complete, and non-redundant regarding to the formal context. Non-redundant means that each attribute implication in the implication base cannot be inferred from the others. However, sometimes some attribute implications in the implication base can be inferred from the others together with a prior knowledge. Regarding knowledge discovery, such attribute implications should be not considered as new knowledge and ignored from the implicational base. In other words, such attribute implications are redundant based on prior knowledge. One sort of prior knowledge is a set of constraints that restricts some attributes in data. In formal context, constraints restrict some attributes of objects in the formal context. This article proposes a method to generate non-redundant implication base of a formal context with some constraints which restricting the formal context. In this case, non-redundant implicational base means that the implicational base does not contain all attribute implications which can be inferred from the others together with information of the constraints. This article also proposes a formulation to check the redundant attribute implications and encoding the problem into satisfiability (SAT) problem such that the problem can be solved by SAT Solver, a software which can solve a SAT problem. After implementation, an experiment shows that the proposed method is able to check the redundant attribute implication and generates a non-redundant implicational base of formal context with constraints.
Collapse
Affiliation(s)
- Taufiq Hidayat
- Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
- Informatics Department, Universitas Islam Indonesia, Yogyakarta, Indonesia
| | - Asmala Ahmad
- Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
| | - Hea Choon Ngo
- Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
| |
Collapse
|
5
|
Anžel A, Heider D, Hattab G. Interactive polar diagrams for model comparison. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107843. [PMID: 37832432 DOI: 10.1016/j.cmpb.2023.107843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/16/2023] [Accepted: 10/02/2023] [Indexed: 10/15/2023]
Abstract
OBJECTIVE Evaluating the performance of multiple complex models, such as those found in biology, medicine, climatology, and machine learning, using conventional approaches is often challenging when using various evaluation metrics simultaneously. The traditional approach, which relies on presenting multi-model evaluation scores in the table, presents an obstacle when determining the similarities between the models and the order of performance. METHODS By combining statistics, information theory, and data visualization, juxtaposed Taylor and Mutual Information Diagrams permit users to track and summarize the performance of one model or a collection of different models. To uncover linear and nonlinear relationships between models, users may visualize one or both charts. RESULTS Our library presents the first publicly available implementation of the Mutual Information Diagram and its new interactive capabilities, as well as the first publicly available implementation of an interactive Taylor Diagram. Extensions have been implemented so that both diagrams can display temporality, multimodality, and multivariate data sets, and feature one scalar model property such as uncertainty. Our library, named polar-diagrams, supports both continuous and categorical attributes. CONCLUSION The library can be used to quickly and easily assess the performances of complex models, such as those found in machine learning, climate, or biomedical domains.
Collapse
Affiliation(s)
- Aleksandar Anžel
- Department of Mathematics & Computer Science, University of Marburg, Hans-Meerwein-Straße 6, Marburg, D-35032, Hesse, Germany.
| | - Dominik Heider
- Department of Mathematics & Computer Science, University of Marburg, Hans-Meerwein-Straße 6, Marburg, D-35032, Hesse, Germany
| | - Georges Hattab
- Center for Artificial Intelligence in Public Health Research (ZKI-PH), Robert Koch-Institute, Nordufer 20, Berlin, 13353, Berlin, Germany; Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 14, Berlin, 14195, Berlin, Germany
| |
Collapse
|
6
|
Ma B, Zhang J, Li X, Zou W. Stochastic photonic spiking neuron for Bayesian inference with unsupervised learning. OPTICS LETTERS 2023; 48:1411-1414. [PMID: 36946940 DOI: 10.1364/ol.484268] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/05/2023] [Indexed: 06/18/2023]
Abstract
Stochasticity is an inherent feature of biological neural activities. We propose a noise-injection scheme to implement a GHz-rate stochastic photonic spiking neuron (S-PSN). The firing-probability encoding is experimentally demonstrated and exploited for Bayesian inference with unsupervised learning. In a breast diagnosis task, the stochastic photonic spiking neural network (S-PSNN) can not only achieve a classification accuracy of 96.6%, but can also evaluate the diagnosis uncertainty with prediction entropies. As a result, the misdiagnosis rate is reduced by 80% compared to that of a conventional deterministic photonic spiking neural network (D-PSNN) for the same task. The GHz-rate S-PSN endows the neuromorphic photonics with high-speed Bayesian inference for reliable information processing in error-critical scenarios.
Collapse
|
7
|
González-Patiño D, Villuendas-Rey Y, Saldaña-Pérez M, Argüelles-Cruz AJ. A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3240. [PMID: 36833936 PMCID: PMC9965500 DOI: 10.3390/ijerph20043240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/23/2023] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification.
Collapse
Affiliation(s)
- David González-Patiño
- Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico
| | - Yenny Villuendas-Rey
- Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Ciudad de México 07700, Mexico
| | - Magdalena Saldaña-Pérez
- Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico
| | | |
Collapse
|
8
|
Zhou Y, Zhou Z, Hooker G. Approximation trees: statistical reproducibility in model distillation. Data Min Knowl Discov 2023. [DOI: 10.1007/s10618-022-00907-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
|
9
|
HS-Gen: a hypersphere-constrained generation mechanism to improve synthetic minority oversampling for imbalanced classification. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00938-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
AbstractMitigating the impact of class-imbalance data on classifiers is a challenging task in machine learning. SMOTE is a well-known method to tackle this task by modifying class distribution and generating synthetic instances. However, most of the SMOTE-based methods focus on the phase of data selection, while few consider the phase of data generation. This paper proposes a hypersphere-constrained generation mechanism (HS-Gen) to improve synthetic minority oversampling. Unlike linear interpolation commonly used in SMOTE-based methods, HS-Gen generates a minority instance in a hypersphere rather than on a straight line. This mechanism expands the distribution range of minority instances with significant randomness and diversity. Furthermore, HS-Gen is attached with a noise prevention strategy that adaptively shrinks the hypersphere by determining whether new instances fall into the majority class region. HS-Gen can be regarded as an oversampling optimization mechanism and flexibly embedded into the SMOTE-based methods. We conduct comparative experiments by embedding HS-Gen into the original SMOTE, Borderline-SMOTE, ADASYN, k-means SMOTE, and RSMOTE. Experimental results show that the embedded versions can generate higher quality synthetic instances than the original ones. Moreover, on these oversampled datasets, the conventional classifiers (C4.5 and Adaboost) obtain significant performance improvement in terms of F1 measure and G-mean.
Collapse
|
10
|
Comsa IM, Potempa K, Versari L, Fischbacher T, Gesmundo A, Alakuijala J. Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5939-5952. [PMID: 33900924 DOI: 10.1109/tnnls.2021.3071976] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We propose a spiking neural network model that encodes information in the relative timing of individual spikes. In classification tasks, the output of the network is indicated by the first neuron to spike in the output layer. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. The network operates using a biologically plausible synaptic transfer function. In addition, we use trainable pulses that provide bias, add flexibility during training, and exploit the decayed part of the synaptic function. We show that such networks can be successfully trained on multiple data sets encoded in time, including MNIST. Our model outperforms comparable spiking models on MNIST and achieves similar quality to fully connected conventional networks with the same architecture. The spiking network spontaneously discovers two operating modes, mirroring the accuracy-speed tradeoff observed in human decision-making: a highly accurate but slow regime, and a fast but slightly lower accuracy regime. These results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. By studying temporal coding in spiking networks, we aim to create building blocks toward energy-efficient, state-based biologically inspired neural architectures. We provide open-source code for the model.
Collapse
|
11
|
Learning Functions and Classes Using Rules. AI 2022. [DOI: 10.3390/ai3030044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In the current work, a novel method is presented for generating rules for data classification as well as for regression problems. The proposed method generates simple rules in a high-level programming language with the help of grammatical evolution. The method does not depend on any prior knowledge of the dataset; the memory it requires for its execution is constant regardless of the objective problem, and it can be used to detect any hidden dependencies between the features of the input problem as well. The proposed method was tested on a extensive range of problems from the relevant literature, and comparative results against other machine learning techniques are presented in this manuscript.
Collapse
|
12
|
A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS). Mach Learn 2022. [DOI: 10.1007/s10994-022-06221-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
AbstractFeature selection reduces the complexity of high-dimensional datasets and helps to gain insights into systematic variation in the data. These aspects are essential in domains that rely on model interpretability, such as life sciences. We propose a (U)ser-Guided (Bay)esian Framework for (F)eature (S)election, UBayFS, an ensemble feature selection technique embedded in a Bayesian statistical framework. Our generic approach considers two sources of information: data and domain knowledge. From data, we build an ensemble of feature selectors, described by a multinomial likelihood model. Using domain knowledge, the user guides UBayFS by weighting features and penalizing feature blocks or combinations, implemented via a Dirichlet-type prior distribution. Hence, the framework combines three main aspects: ensemble feature selection, expert knowledge, and side constraints. Our experiments demonstrate that UBayFS (a) allows for a balanced trade-off between user knowledge and data observations and (b) achieves accurate and robust results.
Collapse
|
13
|
A New Nonparametric Multivariate Control Scheme for Simultaneous Monitoring Changes in Location and Scale. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3385825. [PMID: 35832137 PMCID: PMC9273427 DOI: 10.1155/2022/3385825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 11/18/2022]
Abstract
Real-time monitoring of the breast cancer index is becoming increasingly important. It can help create advances in the diagnosis and treatment of breast cancer. In today's modern medical processes, simultaneously monitoring changes in observations in terms of location and scale are convenient for the implementation of control schemes but can be challenging. In this paper, we consider a new nonparametric control scheme for monitoring location and scale parameters in multivariate processes. The proposed method is easy to implement, and the performance of the proposed control procedure is discussed. Then, we compare the proposed scheme with some competing methods. Simulation results show that the proposed scheme can efficiently detect a range of shifts. The proposed chart can trigger an alert and timely discover the change of the breast cancer index.
Collapse
|
14
|
An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm. SENSORS 2022; 22:s22124401. [PMID: 35746183 PMCID: PMC9228155 DOI: 10.3390/s22124401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/06/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022]
Abstract
Automated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.
Collapse
|
15
|
Hauschild AC, Lemanczyk M, Matschinske J, Frisch T, Zolotareva O, Holzinger A, Baumbach J, Heider D. Federated Random Forests can improve local performance of predictive models for various healthcare applications. Bioinformatics 2022; 38:2278-2286. [PMID: 35139148 DOI: 10.1093/bioinformatics/btac065] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/08/2022] [Accepted: 02/01/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Limited data access has hindered the field of precision medicine from exploring its full potential, e.g. concerning machine learning and privacy and data protection rules.Our study evaluates the efficacy of federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. We addressed three common challenges: (i) number of parties, (ii) sizes of datasets and (iii) imbalanced phenotypes, evaluated on five biomedical datasets. RESULTS The FRF outperformed the average local models and performed comparably to the data-centralized models trained on the entire data. With an increasing number of models and decreasing dataset size, the performance of local models decreases drastically. The FRF, however, do not decrease significantly. When combining datasets of different sizes, the FRF vastly improve compared to the average local models. We demonstrate that the FRF remain more robust and outperform the local models by analyzing different class-imbalances.Our results support that FRF overcome boundaries of clinical research and enables collaborations across institutes without violating privacy or legal regulations. Clinicians benefit from a vast collection of unbiased data aggregated from different geographic locations, demographics and other varying factors. They can build more generalizable models to make better clinical decisions, which will have relevance, especially for patients in rural areas and rare or geographically uncommon diseases, enabling personalized treatment. In combination with secure multi-party computation, federated learning has the power to revolutionize clinical practice by increasing the accuracy and robustness of healthcare AI and thus paving the way for precision medicine. AVAILABILITY AND IMPLEMENTATION The implementation of the federated random forests can be found at https://featurecloud.ai/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
| | - Marta Lemanczyk
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| | - Julian Matschinske
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising-Weihenstephan, Germany.,Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Tobias Frisch
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Olga Zolotareva
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising-Weihenstephan, Germany
| | - Andreas Holzinger
- Institut für Medizinische Informatik, Statistik und Dokumentation, Medizinische Universität Graz, Graz, Austria
| | - Jan Baumbach
- Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Dominik Heider
- Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany
| |
Collapse
|
16
|
Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1612468. [PMID: 35371256 PMCID: PMC8975665 DOI: 10.1155/2022/1612468] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/28/2022] [Accepted: 02/15/2022] [Indexed: 11/21/2022]
Abstract
The hypercube optimization search (HOS) approach is a new efficient and robust metaheuristic algorithm that simulates the dove's movement in quest of new food sites in nature, utilizing hypercubes to depict the search zones. In medical informatics, the classification of medical data is one of the most challenging tasks because of the uncertainty and nature of healthcare data. This paper proposes the use of the HOS algorithm for training multilayer perceptrons (MLP), one of the most extensively used neural networks (NNs), to enhance its efficacy as a decision support tool for medical data classification. The proposed HOS-MLP model is tested on four significant medical datasets: orthopedic patients, diabetes, coronary heart disease, and breast cancer, to assess HOS's success in training MLP. For verification, the results are compared with eleven different classifiers and eight well-regarded MLP trainer metaheuristic algorithms: particle swarm optimization (PSO), biogeography-based optimizer (BBO), the firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), bat algorithm (BAT), monarch butterfly optimizer (MBO), and the flower pollination algorithm (FPA). The experimental results demonstrate that the MLP trained by HOS outperforms the other comparative models regarding mean square error (MSE), classification accuracy, and convergence rate. The findings also reveal that the HOS help the MLP to produce more accurate results than other classification algorithms for the prediction of diseases.
Collapse
|
17
|
Rojas MG, Olivera AC, Vidal PJ. Optimising Multilayer Perceptron weights and biases through a Cellular Genetic Algorithm for medical data classification. ARRAY 2022. [DOI: 10.1016/j.array.2022.100173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
|
18
|
Multilayer Photonic Spiking Neural Networks: Generalized Supervised Learning Algorithm and Network Optimization. PHOTONICS 2022. [DOI: 10.3390/photonics9040217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We propose a generalized supervised learning algorithm for multilayer photonic spiking neural networks (SNNs) by combining the spike-timing dependent plasticity (STDP) rule and the gradient descent mechanism. A vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) is employed as a photonic leaky-integrate-and-fire (LIF) neuron. The temporal coding strategy is employed to transform information into the precise firing time. With the modified supervised learning algorithm, the trained multilayer photonic SNN successfully solves the XOR problem and performs well on the Iris and Wisconsin breast cancer datasets. This indicates that a generalized supervised learning algorithm is realized for multilayer photonic SNN. In addition, network optimization is performed by considering different network sizes.
Collapse
|
19
|
Quadratic discriminant analysis by projection. J MULTIVARIATE ANAL 2022. [DOI: 10.1016/j.jmva.2022.104987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
20
|
Mirzal A. Statistical Analysis of Microarray Data Clustering using NMF, Spectral Clustering, Kmeans, and GMM. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1173-1192. [PMID: 32956065 DOI: 10.1109/tcbb.2020.3025486] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In unsupervised learning literature, the study of clustering using microarray gene expression datasets has been extensively conducted with nonnegative matrix factorization (NMF), spectral clustering, kmeans, and gaussian mixture model (GMM)are some of the most used methods. However, there is still a limited number of works that utilize statistical analysis to measure the significances of performance differences between these methods. In this paper, statistical analysis of performance differences between ten NMF, six spectral clustering, four GMM, and the standard kmeans algorithms in clustering eleven publicly available microarray gene expression datasets with the number of clusters ranges from two to ten is presented. The experimental results show that statistically NMFs and kmeans have similar performances and outperform spectral clustering. As spectral clustering can be used to uncover hidden manifold structures, the underperformance of spectral methods leads us to question whether the datasets have manifold structures. Visual inspection using multidimensional scaling plots indicates that such structures do not exist. Moreover, as the plots indicate that clusters in some datasets have elliptical boundaries, GMM methods are also utilized. The experimental results show that GMM methods outperform the other methods to some degree, and thus imply that the datasets follow gaussian distributions.
Collapse
|
21
|
Nguyen N, Chawshin K, Berg CF, Varagnolo D. Shuffle & untangle: novel untangle methods for solving the tanglegram layout problem. BIOINFORMATICS ADVANCES 2022; 2:vbac014. [PMID: 36699369 PMCID: PMC9710592 DOI: 10.1093/bioadv/vbac014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 02/16/2022] [Accepted: 02/28/2022] [Indexed: 01/28/2023]
Abstract
Motivation A tanglegram is a plot of two-tree-like diagrams, one facing the other, and having their labels connected by inter-tree edges. These two trees, which could be both phylogenetic trees and dendrograms stemming from hierarchical clusterings, have thus identically labelled leaves but different topologies. As a result, the inter-tree edges of a tanglegram can be intricately tangled and difficult to be analysed and explained by human readers. To better visualize the tanglegram (and thus compare the two dendrograms) one may try to untangle it, i.e. search for that series of flippings of the various branches of the two trees that minimizes the number of crossings among the inter-tree edges. The untanglement problem has received significant interest in the past decade, and several techniques have been proposed to address it. These techniques are computationally efficient but tend to fail at finding the global optimum configuration generating the least tangly tanglegram. Results We leverage the existing results to propose untanglement methods that are characterized by an overall slower convergence method than the ones in the literature, but that produce tanglegrams with lower entanglements. Availability and implementation One of the algorithms is implemented in Python, and available from https://github.com/schlegelp/tanglegram.
Collapse
Affiliation(s)
- Nghia Nguyen
- Department of Geoscience and Petroleum, NTNU, 7031 Trondheim, Norway
| | | | - Carl Fredrik Berg
- Department of Geoscience and Petroleum, NTNU, 7031 Trondheim, Norway,To whom correspondence should be addressed.
| | - Damiano Varagnolo
- Department of Engineering Cybernetics, NTNU, 7034 Trondheim, Norway,Department of Information Engineering, University of Padova, 35122 Padova, Italy
| |
Collapse
|
22
|
A Two-Phase Evolutionary Method to Train RBF Networks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12052439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article proposes a two-phase hybrid method to train RBF neural networks for classification and regression problems. During the first phase, a range for the critical parameters of the RBF network is estimated and in the second phase a genetic algorithm is incorporated to locate the best RBF neural network for the underlying problem. The method is compared against other training methods of RBF neural networks on a wide series of classification and regression problems from the relevant literature and the results are reported.
Collapse
|
23
|
Asenso TQ, Wang P, Zhang H. Pliable lasso for the support vector machine. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2022.2032160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
| | - Puyu Wang
- School of Mathematics, Northwest University, Xi’an, China
| | - Hai Zhang
- School of Mathematics, Northwest University, Xi’an, China
- Faculty of Information Technology, Macau University of Science and Technology, Macau, China
| |
Collapse
|
24
|
Bertsimas D, Digalakis V. The backbone method for ultra-high dimensional sparse machine learning. Mach Learn 2022. [DOI: 10.1007/s10994-021-06123-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
25
|
Synthetic sampling from small datasets: A modified mega-trend diffusion approach using k-nearest neighbors. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
26
|
Beinecke J, Heider D. Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making. BioData Min 2021; 14:49. [PMID: 34844620 PMCID: PMC8628399 DOI: 10.1186/s13040-021-00283-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/10/2021] [Indexed: 02/08/2023] Open
Abstract
Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values. While feature selection approaches and imputation techniques address the former problems, the class imbalance is typically addressed using augmentation techniques. However, these techniques have been developed for big data analytics, and their suitability for clinical data sets is unclear.This study analyzed different augmentation techniques for use in clinical data sets and subsequent employment of machine learning-based classification. It turns out that Gaussian Noise Up-Sampling (GNUS) is not always but generally, is as good as SMOTE and ADASYN and even outperform those on some datasets. However, it has also been shown that augmentation does not improve classification at all in some cases.
Collapse
Affiliation(s)
- Jacqueline Beinecke
- Department of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, 35043, Marburg, Germany
| | - Dominik Heider
- Department of Mathematics and Computer Science, Philipps-University of Marburg, Hans-Meerwein-Str. 6, 35043, Marburg, Germany.
| |
Collapse
|
27
|
A Rule Extraction Technique Applied to Ensembles of Neural Networks, Random Forests, and Gradient-Boosted Trees. ALGORITHMS 2021. [DOI: 10.3390/a14120339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In machine learning, ensembles of models based on Multi-Layer Perceptrons (MLPs) or decision trees are considered successful models. However, explaining their responses is a complex problem that requires the creation of new methods of interpretation. A natural way to explain the classifications of the models is to transform them into propositional rules. In this work, we focus on random forests and gradient-boosted trees. Specifically, these models are converted into an ensemble of interpretable MLPs from which propositional rules are produced. The rule extraction method presented here allows one to precisely locate the discriminating hyperplanes that constitute the antecedents of the rules. In experiments based on eight classification problems, we compared our rule extraction technique to “Skope-Rules” and other state-of-the-art techniques. Experiments were performed with ten-fold cross-validation trials, with propositional rules that were also generated from ensembles of interpretable MLPs. By evaluating the characteristics of the extracted rules in terms of complexity, fidelity, and accuracy, the results obtained showed that our rule extraction technique is competitive. To the best of our knowledge, this is one of the few works showing a rule extraction technique that has been applied to both ensembles of decision trees and neural networks.
Collapse
|
28
|
Gandouz M, Holzmann H, Heider D. Machine learning with asymmetric abstention for biomedical decision-making. BMC Med Inform Decis Mak 2021; 21:294. [PMID: 34702225 PMCID: PMC8549182 DOI: 10.1186/s12911-021-01655-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/13/2021] [Indexed: 02/08/2023] Open
Abstract
Machine learning and artificial intelligence have entered biomedical decision-making for diagnostics, prognostics, or therapy recommendations. However, these methods need to be interpreted with care because of the severe consequences for patients. In contrast to human decision-making, computational models typically make a decision also with low confidence. Machine learning with abstention better reflects human decision-making by introducing a reject option for samples with low confidence. The abstention intervals are typically symmetric intervals around the decision boundary. In the current study, we use asymmetric abstention intervals, which we demonstrate to be better suited for biomedical data that is typically highly imbalanced. We evaluate symmetric and asymmetric abstention on three real-world biomedical datasets and show that both approaches can significantly improve classification performance. However, asymmetric abstention rejects as many or fewer samples compared to symmetric abstention and thus, should be used in imbalanced data.
Collapse
Affiliation(s)
- Mariem Gandouz
- Department of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, University of Marburg, 35032, Marburg, Germany
| | - Hajo Holzmann
- Department of Statistics, Faculty of Mathematics and Computer Science, University of Marburg, 35032, Marburg, Germany
| | - Dominik Heider
- Department of Data Science in Biomedicine, Faculty of Mathematics and Computer Science, University of Marburg, 35032, Marburg, Germany.
| |
Collapse
|
29
|
Cai S, Zhou J, Pan J. Estimating the sample mean and standard deviation from order statistics and sample size in meta-analysis. Stat Methods Med Res 2021; 30:2701-2719. [PMID: 34668458 DOI: 10.1177/09622802211047348] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In recent years, a growing number of researchers have attempted to overcome the constraints of size and scope in different medical studies to find out the overall treatment effects. As a widespread technique to combine results of multiple studies, commonly used meta-analytic approaches for continuous outcomes demand sample means and standard deviations of primary studies, which are absent sometimes, especially when the outcome is skewed. Instead, the median, the extrema, and/or the quartiles are reported. One feasible solution is to convert the preceding order statistics to demanded statistics to keep effect measures consistent. In this article, we propose new methods based on maximum likelihood estimation for known distributions with unknown parameters. For unknown underlying distributions, the Box-Cox transformation is applied to the reported order statistics so that the techniques for normal distribution can be utilized. Two approaches for estimating the power parameter in Box-Cox transformation are provided. Both simulation studies and real data analysis indicate that in most cases, the proposed methods outperform the existing methods in estimation accuracy.
Collapse
Affiliation(s)
- Siyu Cai
- College of Mathematics, 12530Sichuan University, Chengdu, Sichuan, China
| | - Jie Zhou
- College of Mathematics, 12530Sichuan University, Chengdu, Sichuan, China.,Med-X Center for Informatics, 12530Sichuan University, Chengdu, Sichuan, China
| | - Jianxin Pan
- School of Mathematics, 5292University of Manchester, Manchester, UK
| |
Collapse
|
30
|
Qiao Z, Shan W, Jiang N, Heidari AA, Chen H, Teng Y, Turabieh H, Mafarja M. Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns. INT J INTELL SYST 2021. [DOI: 10.1002/int.22658] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Zenglin Qiao
- School of Emergency Management, Institute of Disaster Prevention Langfang China
| | - Weifeng Shan
- School of Emergency Management, Institute of Disaster Prevention Langfang China
- Institute of Geophysics, China Earthquake Administration Beijing China
| | - Nan Jiang
- College of Information Engineering, East China Jiaotong University Nanchang Jiangxi China
| | - Ali Asghar Heidari
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China
| | - Yuntian Teng
- Institute of Geophysics, China Earthquake Administration Beijing China
| | - Hamza Turabieh
- Department of Information Technology College of Computers and Information Technology, Taif University Taif Saudi Arabia
| | - Majdi Mafarja
- Department of Computer Science Birzeit University West Bank Palestine
| |
Collapse
|
31
|
Qian X, Zhou Z, Hu J, Zhu J, Huang H, Dai Y. A comparative study of kernel-based vector machines with probabilistic outputs for medical diagnosis. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
32
|
Learnability and robustness of shallow neural networks learned by a performance-driven BP and a variant of PSO for edge decision-making. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06019-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
33
|
|
34
|
Yue J, Liu L. A dynamic sampling for monitoring nonparametric multivariate processes. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1945628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Jin Yue
- School of Mathematics and VC&VR Lab, Sichuan Normal University, Chengdu, Sichuan, China
| | - Liu Liu
- School of Mathematics and VC&VR Lab, Sichuan Normal University, Chengdu, Sichuan, China
| |
Collapse
|
35
|
Rahman M, Ghasemi Y, Suley E, Zhou Y, Wang S, Rogers J. Machine Learning Based Computer Aided Diagnosis of Breast Cancer Utilizing Anthropometric and Clinical Features. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.05.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
|
36
|
Gangopadhyay A, Chakrabartty S. A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons. Front Neurosci 2021; 15:715451. [PMID: 34393719 PMCID: PMC8355563 DOI: 10.3389/fnins.2021.715451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
Growth-transform (GT) neurons and their population models allow for independent control over the spiking statistics and the transient population dynamics while optimizing a physically plausible distributed energy functional involving continuous-valued neural variables. In this paper we describe a backpropagation-less learning approach to train a network of spiking GT neurons by enforcing sparsity constraints on the overall network spiking activity. The key features of the model and the proposed learning framework are: (a) spike responses are generated as a result of constraint violation and hence can be viewed as Lagrangian parameters; (b) the optimal parameters for a given task can be learned using neurally relevant local learning rules and in an online manner; (c) the network optimizes itself to encode the solution with as few spikes as possible (sparsity); (d) the network optimizes itself to operate at a solution with the maximum dynamic range and away from saturation; and (e) the framework is flexible enough to incorporate additional structural and connectivity constraints on the network. As a result, the proposed formulation is attractive for designing neuromorphic tinyML systems that are constrained in energy, resources, and network structure. In this paper, we show how the approach could be used for unsupervised and supervised learning such that minimizing a training error is equivalent to minimizing the overall spiking activity across the network. We then build on this framework to implement three different multi-layer spiking network architectures with progressively increasing flexibility in training and consequently, sparsity. We demonstrate the applicability of the proposed algorithm for resource-efficient learning using a publicly available machine olfaction dataset with unique challenges like sensor drift and a wide range of stimulus concentrations. In all of these case studies we show that a GT network trained using the proposed learning approach is able to minimize the network-level spiking activity while producing classification accuracy that are comparable to standard approaches on the same dataset.
Collapse
Affiliation(s)
| | - Shantanu Chakrabartty
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, United States
| |
Collapse
|
37
|
Mojirsheibani M, Pouliot W. A nearest-neighbor-based ensemble classifier and its large-sample optimality. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2021.1882458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
| | - William Pouliot
- Department of Economics, University of Birmingham, Birmingham, UK
| |
Collapse
|
38
|
Widening: using parallel resources to improve model quality. Data Min Knowl Discov 2021. [DOI: 10.1007/s10618-021-00749-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractThis paper provides a unified description of Widening, a framework for the use of parallel (or otherwise abundant) computational resources to improve model quality. We discuss different theoretical approaches to Widening with and without consideration of diversity. We then soften some of the underlying constraints so that Widening can be implemented in real world algorithms. We summarize earlier experimental results demonstrating the potential impact as well as promising implementation strategies before concluding with a survey of related work.
Collapse
|
39
|
Fang J, Yi GY. Imputation and likelihood methods for matrix‐variate logistic regression with response misclassification. CAN J STAT 2021. [DOI: 10.1002/cjs.11620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Junhan Fang
- Department of Statistics and Actuarial Science University of Waterloo Waterloo Ontario Canada
| | - Grace Y. Yi
- Department of Statistics and Actuarial Science University of Waterloo Waterloo Ontario Canada
- Department of Computer Science University of Western Ontario London Ontario Canada
| |
Collapse
|
40
|
Omri N, Al Masry Z, Mairot N, Giampiccolo S, Zerhouni N. Towards an adapted PHM approach: Data quality requirements methodology for fault detection applications. COMPUT IND 2021. [DOI: 10.1016/j.compind.2021.103414] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
41
|
Zhang Z, Chen B, Xu S, Chen G, Xie J. A novel voting convergent difference neural network for diagnosing breast cancer. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.01.083] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
42
|
Gardner B, Grüning A. Supervised Learning With First-to-Spike Decoding in Multilayer Spiking Neural Networks. Front Comput Neurosci 2021; 15:617862. [PMID: 33912021 PMCID: PMC8072060 DOI: 10.3389/fncom.2021.617862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/08/2021] [Indexed: 11/18/2022] Open
Abstract
Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strategies to rapidly and efficiently represent sensory stimuli. Accordingly, it would be desirable to apply spike-based computation to tackling real-world challenges, and in particular transferring such theory to neuromorphic systems for low-power embedded applications. Motivated by this, we propose a new supervised learning method that can train multilayer spiking neural networks to solve classification problems based on a rapid, first-to-spike decoding strategy. The proposed learning rule supports multiple spikes fired by stochastic hidden neurons, and yet is stable by relying on first-spike responses generated by a deterministic output layer. In addition to this, we also explore several distinct, spike-based encoding strategies in order to form compact representations of presented input data. We demonstrate the classification performance of the learning rule as applied to several benchmark datasets, including MNIST. The learning rule is capable of generalizing from the data, and is successful even when used with constrained network architectures containing few input and hidden layer neurons. Furthermore, we highlight a novel encoding strategy, termed "scanline encoding," that can transform image data into compact spatiotemporal patterns for subsequent network processing. Designing constrained, but optimized, network structures and performing input dimensionality reduction has strong implications for neuromorphic applications.
Collapse
Affiliation(s)
- Brian Gardner
- Department of Computer Science, University of Surrey, Guildford, United Kingdom
| | - André Grüning
- Faculty of Electrical Engineering and Computer Science, University of Applied Sciences, Stralsund, Germany
| |
Collapse
|
43
|
WOLIF: An efficiently tuned classifier that learns to classify non-linear temporal patterns without hidden layers. APPL INTELL 2021. [DOI: 10.1007/s10489-020-01934-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
44
|
Mohindru G, Mondal K, Banka H. Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2021. [DOI: 10.1049/cit2.12032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Gaurav Mohindru
- Department of Computer Science and Engineering IIT(ISM)Dhanbad India
| | | | - Haider Banka
- Department of Computer Science and Engineering IIT(ISM)Dhanbad India
| |
Collapse
|
45
|
Ramanathan TV, Pandhare SC. The Focused Information Criterion for Logistic Time Series Regression Models Under Locally Biased Estimating Functions. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2021. [DOI: 10.1007/s42519-021-00169-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
46
|
Wang K, Hu Q, Gao B, Lin Q, Zhuge FW, Zhang DY, Wang L, He YH, Scheicher RH, Tong H, Miao XS. Threshold switching memristor-based stochastic neurons for probabilistic computing. MATERIALS HORIZONS 2021; 8:619-629. [PMID: 34821279 DOI: 10.1039/d0mh01759k] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Biological neurons exhibit dynamic excitation behavior in the form of stochastic firing, rather than stiffly giving out spikes upon reaching a fixed threshold voltage, which empowers the brain to perform probabilistic inference in the face of uncertainty. However, owing to the complexity of the stochastic firing process in biological neurons, the challenge of fabricating and applying stochastic neurons with bio-realistic dynamics to probabilistic scenarios remains to be fully addressed. In this work, a novel CuS/GeSe conductive-bridge threshold switching memristor is fabricated and singled out to realize electronic stochastic neurons, which is ascribed to the similarity between the stochastic switching behavior observed in the device and that of biological ion channels. The corresponding electric circuit of a stochastic neuron is then constructed and the probabilistic firing capacity of the neuron is utilized to implement Bayesian inference in a spiking neural network (SNN). The application prospects are demonstrated on the example of a tumor diagnosis task, where common fatal diagnostic errors of a conventional artificial neural network are successfully circumvented. Moreover, in comparison to deterministic neuron-based SNNs, the stochastic neurons enable SNNs to deliver an estimate of the uncertainty in their predictions, and the fidelity of the judgement is drastically improved by 81.2%.
Collapse
Affiliation(s)
- Kuan Wang
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
47
|
ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost. NUCLEAR ENGINEERING AND TECHNOLOGY 2021. [DOI: 10.1016/j.net.2020.04.008] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
48
|
A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis. Processes (Basel) 2020. [DOI: 10.3390/pr8121565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods.
Collapse
|
49
|
Zhang P, Wu J, Zhai H, Li S. ABCModeller: an automatic data mining tool based on a consistent voting method with a user-friendly graphical interface. Brief Bioinform 2020; 22:5924101. [PMID: 33057581 DOI: 10.1093/bib/bbaa247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 08/27/2020] [Accepted: 09/03/2020] [Indexed: 02/06/2023] Open
Abstract
In order to extract useful information from a huge amount of biological data nowadays, simple and convenient tools are urgently needed for data analysis and modeling. In this paper, an automatic data mining tool, termed as ABCModeller (Automatic Binary Classification Modeller), with a user-friendly graphical interface was developed here, which includes automated functions as data preprocessing, significant feature extraction, classification modeling, model evaluation and prediction. In order to enhance the generalization ability of the final model, a consistent voting method was built here in this tool with the utilization of three popular machine-learning algorithms, as artificial neural network, support vector machine and random forest. Besides, Fibonacci search and orthogonal experimental design methods were also employed here to automatically select significant features in the data space and optimal hyperparameters of the three algorithms to achieve the best model. The reliability of this tool has been verified through multiple benchmark data sets. In addition, with the advantage of a user-friendly graphical interface of this tool, users without any programming skills can easily obtain reliable models directly from original data, which can reduce the complexity of modeling and data mining, and contribute to the development of related research including but not limited to biology. The excitable file of this tool can be downloaded from http://lishuyan.lzu.edu.cn/ABCModeller.rar.
Collapse
|
50
|
Wang Q, Guo A. An efficient variance estimator of AUC and its applications to binary classification. Stat Med 2020; 39:4281-4300. [PMID: 32914457 DOI: 10.1002/sim.8725] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 05/05/2020] [Accepted: 07/19/2020] [Indexed: 11/11/2022]
Abstract
The area under the ROC (receiver operating characteristic) curve, AUC, is one of the most commonly used measures to evaluate the performance of a binary classifier. Due to sampling variation, the model with the largest observed AUC score is not necessarily optimal, so it is crucial to assess the variation of AUC estimate. We extend the proposal by Wang and Lindsay and devise an unbiased variance estimator of AUC estimate that is of a two-sample U-statistic form. The proposal can be easily generalized to estimate the variance of a K-sample U-statistic (K ≥ 2). To make our developed variance estimator more applicable, we employ a partition-resampling scheme that is computationally efficient. Simulation studies suggest that the developed AUC variance estimator yields much better or comparable performance to jackknife and bootstrap variance estimators, and computational times that are about 10 to 30 times faster than the times of its counterparts. In practice, the proposal can be used in the one-standard-error rule for model selection, or to construct an asymptotic confidence interval of AUC in binary classification. In addition to conducting simulation studies, we illustrate its practical applications using two real datasets in medical sciences.
Collapse
Affiliation(s)
- Qing Wang
- Department of Mathematics, Wellesley College, Wellesley, Massachusetts
| | - Alexandria Guo
- Department of Mathematics, Wellesley College, Wellesley, Massachusetts
| |
Collapse
|