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Barata C, Rotemberg V, Codella NCF, Tschandl P, Rinner C, Akay BN, Apalla Z, Argenziano G, Halpern A, Lallas A, Longo C, Malvehy J, Puig S, Rosendahl C, Soyer HP, Zalaudek I, Kittler H. A reinforcement learning model for AI-based decision support in skin cancer. Nat Med 2023; 29:1941-1946. [PMID: 37501017 PMCID: PMC10427421 DOI: 10.1038/s41591-023-02475-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 06/28/2023] [Indexed: 07/29/2023]
Abstract
We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards and penalties based on expert-generated tables, balancing the benefits and harms of various diagnostic errors, which were applied using reinforcement learning. Compared with supervised learning, the reinforcement learning model improved the sensitivity for melanoma from 61.4% to 79.5% (95% confidence interval (CI): 73.5-85.6%) and for basal cell carcinoma from 79.4% to 87.1% (95% CI: 80.3-93.9%). AI overconfidence was also reduced while simultaneously maintaining accuracy. Reinforcement learning increased the rate of correct diagnoses made by dermatologists by 12.0% (95% CI: 8.8-15.1%) and improved the rate of optimal management decisions from 57.4% to 65.3% (95% CI: 61.7-68.9%). We further demonstrated that the reward-adjusted reinforcement learning model and a threshold-based model outperformed naïve supervised learning in various clinical scenarios. Our findings suggest the potential for incorporating human preferences into image-based diagnostic algorithms.
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Affiliation(s)
- Catarina Barata
- Institute for Systems and Robotics, LARSyS, Instituto Superior Técnico, Lisbon, Portugal
| | - Veronica Rotemberg
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Philipp Tschandl
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Christoph Rinner
- Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Vienna, Austria
| | - Bengu Nisa Akay
- Ankara University School of Medicine, Department of Dermatology, Ankara, Turkey
| | - Zoe Apalla
- Second Department of Dermatology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Allan Halpern
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Aimilios Lallas
- Second Department of Dermatology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Caterina Longo
- Dermatology Unit, University of Modena and Reggio Emilia, Modena, Italy
- Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Centro Oncologico ad Alta Tecnologia Diagnostica-Dermatologia, Reggio Emilia, Italy
| | - Josep Malvehy
- Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBER ER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Susana Puig
- Melanoma Unit, Dermatology Department, Hospital Clínic Barcelona, Universitat de Barcelona, IDIBAPS, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBER ER), Instituto de Salud Carlos III, Barcelona, Spain
| | - Cliff Rosendahl
- General Practice Clinical Unit, Medical School, The University of Queensland, Brisbane, Queensland, Australia
| | - H Peter Soyer
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, Queensland, Australia
| | - Iris Zalaudek
- Department of Dermatology, Medical University of Trieste, Trieste, Italy
| | - Harald Kittler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria.
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Zhou S, Gu Y, Yu H, Yang X, Gao S. RUE: A Robust Personalized Cost Assignment Strategy for Class Imbalance Cost-sensitive Learning. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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3
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Liu B, Gao F, Li Y. Cost-Sensitive YOLOv5 for Detecting Surface Defects of Industrial Products. SENSORS (BASEL, SWITZERLAND) 2023; 23:2610. [PMID: 36904815 PMCID: PMC10007231 DOI: 10.3390/s23052610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Owing to the remarkable development of deep learning algorithms, defect detection techniques based on deep neural networks have been extensively applied in industrial production. Most existing surface defect detection models assign equal costs to the classification errors among different defect categories but do not strictly distinguish them. However, various errors can generate a great discrepancy in decision risk or classification costs and then produce a cost-sensitive issue that is crucial to the manufacturing process. To address this engineering challenge, we propose a novel supervised classification cost-sensitive learning method (SCCS) and apply it to improve YOLOv5 as CS-YOLOv5, where the classification loss function of object detection was reconstructed according to a new cost-sensitive learning criterion explained by a label-cost vector selection method. In this way, the classification risk information from a cost matrix is directly introduced into the detection model and fully exploited in training. As a result, the developed approach can make low-risk classification decisions for defect detection. It is applicable for direct cost-sensitive learning based on a cost matrix to implement detection tasks. Using two datasets of a painting surface and a hot-rolled steel strip surface, our CS-YOLOv5 model outperforms the original version with respect to cost under different positive classes, coefficients, and weight ratios, but also maintains effective detection performance measured by mAP and F1 scores.
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Bhadra N, Chatterjee SK, Das S. Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data. PLoS One 2023; 18:e0285321. [PMID: 37141215 PMCID: PMC10159166 DOI: 10.1371/journal.pone.0285321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/19/2023] [Indexed: 05/05/2023] Open
Abstract
Plant electrophysiological response contains useful signature of its environment and health which can be utilized using suitable statistical analysis for developing an inverse model to classify the stimulus applied to the plant. In this paper, we have presented a statistical analysis pipeline to tackle a multiclass environmental stimuli classification problem with unbalanced plant electrophysiological data. The objective here is to classify three different environmental chemical stimuli, using fifteen statistical features, extracted from the plant electrical signals and compare the performance of eight different classification algorithms. A comparison using reduced dimensional projection of the high dimensional features via principal component analysis (PCA) has also been presented. Since the experimental data is highly unbalanced due to varying length of the experiments, we employ a random under-sampling approach for the two majority classes to create an ensemble of confusion matrices to compare the classification performances. Along with this, three other multi-classification performance metrics commonly used for unbalanced data viz. balanced accuracy, F1-score and Matthews correlation coefficient have also been analyzed. From the stacked confusion matrices and the derived performance metrics, we choose the best feature-classifier setting in terms of the classification performances carried out in the original high dimensional vs. the reduced feature space, for this highly unbalanced multiclass problem of plant signal classification due to different chemical stress. Difference in the classification performances in the high vs. reduced dimensions are also quantified using the multivariate analysis of variance (MANOVA) hypothesis testing. Our findings have potential real-world applications in precision agriculture for exploring multiclass classification problems with highly unbalanced datasets, employing a combination of existing machine learning algorithms. This work also advances existing studies on environmental pollution level monitoring using plant electrophysiological data.
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Affiliation(s)
- Nivedita Bhadra
- Department of Physical Sciences, Indian Institute of Science Education and Research, Nadia, Kolkata, West Bengal, India
| | - Shre Kumar Chatterjee
- Department of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
| | - Saptarshi Das
- Centre for Environmental Mathematics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom
- Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter, United Kingdom
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5
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Hamdan S, Love BC, von Polier GG, Weis S, Schwender H, Eickhoff SB, Patil KR. Confound-leakage: confound removal in machine learning leads to leakage. Gigascience 2022; 12:giad071. [PMID: 37776368 PMCID: PMC10541796 DOI: 10.1093/gigascience/giad071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 06/01/2023] [Accepted: 08/17/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Machine learning (ML) approaches are a crucial component of modern data analysis in many fields, including epidemiology and medicine. Nonlinear ML methods often achieve accurate predictions, for instance, in personalized medicine, as they are capable of modeling complex relationships between features and the target. Problematically, ML models and their predictions can be biased by confounding information present in the features. To remove this spurious signal, researchers often employ featurewise linear confound regression (CR). While this is considered a standard approach for dealing with confounding, possible pitfalls of using CR in ML pipelines are not fully understood. RESULTS We provide new evidence that, contrary to general expectations, linear confound regression can increase the risk of confounding when combined with nonlinear ML approaches. Using a simple framework that uses the target as a confound, we show that information leaked via CR can increase null or moderate effects to near-perfect prediction. By shuffling the features, we provide evidence that this increase is indeed due to confound-leakage and not due to revealing of information. We then demonstrate the danger of confound-leakage in a real-world clinical application where the accuracy of predicting attention-deficit/hyperactivity disorder is overestimated using speech-derived features when using depression as a confound. CONCLUSIONS Mishandling or even amplifying confounding effects when building ML models due to confound-leakage, as shown, can lead to untrustworthy, biased, and unfair predictions. Our expose of the confound-leakage pitfall and provided guidelines for dealing with it can help create more robust and trustworthy ML models.
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Affiliation(s)
- Sami Hamdan
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Bradley C Love
- Department of Experimental Psychology, University College London, WC1H 0AP London, UK
- The Alan Turing Institute, London NW1 2DB, UK
- European Lab for Learning & Intelligent Systems (ELLIS), WC1E 6BT, London, UK
| | - Georg G von Polier
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, 60528 Frankfurt, Germany
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
| | - Susanne Weis
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Holger Schwender
- Institute of Mathematics, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany
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6
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Wang X, Bao L, Jiang M, Li D, Xu L, Bai M. Toxic mechanism of the Mongolian medicine "Hunqile-7" based on metabonomics and the metabolism of intestinal flora. Toxicol Res (Camb) 2022; 12:49-61. [PMID: 36866222 PMCID: PMC9972816 DOI: 10.1093/toxres/tfac081] [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: 07/04/2022] [Revised: 11/04/2022] [Accepted: 11/18/2022] [Indexed: 12/27/2022] Open
Abstract
The traditional Mongolian medicine Hunqile-7 (HQL-7), which is mainly used to relieve pain in clinic, has certain toxicity. Therefore, toxicological investigation of HQL-7 is of great significance to its safety assessment. In this study, the toxic mechanism of HQL-7 was explored based on a combination of metabolomics and intestinal flora metabolism. UHPLC-MS was used to analyze the serum, liver and kidney samples of rats after intragastric administration of HQL-7. The decision tree and K Nearest Neighbor (KNN) model were established based on the bootstrap aggregation (bagging) algorithm to classify the omics data. After samples were extracted from rat feces, the high-throughput sequencing platform was used to analyze the 16s rRNA V3-V4 region of bacteria. The experimental results confirm that the bagging algorithm improved the classification accuracy. The toxic dose, toxic intensity, and toxic target organ of HQL-7 were determined in toxicity tests. Seventeen biomarkers were identified and the metabolism dysregulation of these biomarkers may be responsible for the toxicity of HQL-7 in vivo. Several kinds of bacteria was demonstrated to be closely related to the physiological indices of renal and liver function, indicating liver and kidney damage induced by HQL-7 may be related to the disturbance of these intestinal bacteria. Overall, the toxic mechanism of HQL-7 was revealed in vivo, which not only provides a scientific basis for the safe and rational clinical use of HQL-7, but also opens up a new field of research on big data for Mongolian medicine.
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Affiliation(s)
- Xiye Wang
- College of Chemistry and Materials Science, Inner Mongolia Minzu University, Tongliao 028000, China,Inner Mongolia Key Laboratory of Chemistry for Natural Products Chemistry and Synthesis for Functional Molecules, Inner Mongolia Minzu University, Tongliao 028000, China
| | - Leer Bao
- Inner Mongolia Autonomous Region Drug Inspection Center, Hohhot 010000, China
| | - Mingyang Jiang
- College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao 028000, China
| | - Dan Li
- College of Chemistry and Materials Science, Inner Mongolia Minzu University, Tongliao 028000, China,Inner Mongolia Key Laboratory of Chemistry for Natural Products Chemistry and Synthesis for Functional Molecules, Inner Mongolia Minzu University, Tongliao 028000, China
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7
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Dai Q, Liu J, Yang J. Multi‐armed bandit heterogeneous ensemble learning for imbalanced data. Comput Intell 2022. [DOI: 10.1111/coin.12566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Qi Dai
- Department of Automation, College of Information Science and Engineering Beijing China
| | - Jian‐wei Liu
- Department of Automation, College of Information Science and Engineering Beijing China
| | - Jiapeng Yang
- College of Science North China University of Science and Technology Tangshan China
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8
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Wang X, Jiang M, Li D, Xu L. Analyzing the Therapeutic Mechanism of Mongolian Medicine Zhonglun-5 in Rheumatoid Arthritis Using a Bagging Algorithm with Serum Metabonomics. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:5997562. [PMID: 36532854 PMCID: PMC9750765 DOI: 10.1155/2022/5997562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/07/2022] [Accepted: 11/23/2022] [Indexed: 10/07/2023]
Abstract
Rheumatoid arthritis (RA) is a complex autoimmune disorder. Zhonglun-5 (ZL), a traditional Mongolian medicine, exhibits an excellent clinical effect on RA; however, its molecular mechanism remains unclear. In this study, rat serum metabolomic analysis was performed to identify potential biomarkers for RA and investigate its treatment mechanism. A Dionex Ultimate 3000 ultrahigh-performance liquid chromatography system coupled with a Q-Exactive Focus Orbitrap mass spectrometer was used for metabonomics analysis. Bootstrap aggregation (bagging) classification algorithm was applied to process data from control (CG), model (MG), and treatment administration groups. The classification accuracy was 100.00% (6/6) in the decision tree model and 83.33% (5/6) in the K-nearest neighbor (KNN) model, accompanied by 18 training samples and 6 testing samples. Using volcanic map analysis, 24 biomarkers were identified between CG and MG, including those related to glycosphingolipid biosynthesis, arachidonic acid, fatty acids, amino acids, bile acids, vitamins, and sphingolipids. A set diagram of the heatmap and drug-biomarker network of potential biomarkers was constructed. After ZL administration, the levels of these biomarkers returned to normal, indicating that ZL had a therapeutic effect in rats with RA. This study established a solid theoretical foundation to promote further research on the clinical applicability of ZL.
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Affiliation(s)
- Xiye Wang
- College of Chemistry and Materials Science, Inner Mongolia Minzu University, Tongliao 028000, China
- Inner Mongolia Key Laboratory of Chemistry for Natural Products Chemistry and Synthesis for Functional Molecules, Tongliao 028000, China
| | - Mingyang Jiang
- College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao 028000, China
| | - Dan Li
- College of Chemistry and Materials Science, Inner Mongolia Minzu University, Tongliao 028000, China
- Inner Mongolia Key Laboratory of Chemistry for Natural Products Chemistry and Synthesis for Functional Molecules, Tongliao 028000, China
| | - Liang Xu
- Inner Mongolia Key Laboratory of Chemistry for Natural Products Chemistry and Synthesis for Functional Molecules, Tongliao 028000, China
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9
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Ghaderi Zefrehi H, Altınçay H. MaMiPot: a paradigm shift for the classification of imbalanced data. J Intell Inf Syst 2022. [DOI: 10.1007/s10844-022-00763-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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10
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Han M, Li A, Gao Z, Mu D, Liu S. A survey of multi-class imbalanced data classification methods. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, and the minority class in some applications contains the significant information. So far, many classification methods and strategies for binary imbalanced data have been proposed, but there are still many problems and challenges in multi-class imbalanced data that need to be solved urgently. The classification methods for multi-class imbalanced data are analyzed and summarized in terms of data preprocessing methods and algorithm-level classification methods, and the performance of the algorithms using the same dataset is compared separately. In the data preprocessing methods, the methods of oversampling, under-sampling, hybrid sampling and feature selection are mainly introduced. Algorithm-level classification methods are comprehensively introduced in four aspects: ensemble learning, neural network, support vector machine and multi-class decomposition technique. At the same time, all data preprocessing methods and algorithm-level classification methods are analyzed in detail in terms of the techniques used, comparison algorithms, pros and cons, respectively. Moreover, the evaluation metrics commonly used for multi-class imbalanced data classification methods are described comprehensively. Finally, the future directions of multi-class imbalanced data classification are given.
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Affiliation(s)
- Meng Han
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
| | - Ang Li
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
| | - Zhihui Gao
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
| | - Dongliang Mu
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
| | - Shujuan Liu
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
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11
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Deb S, Warule P, Nair A, Sultan H, Dash R, Krajewski J. Detection of Common Cold from Speech Signals using Deep Neural Network. CIRCUITS, SYSTEMS, AND SIGNAL PROCESSING 2022; 42:1707-1722. [PMID: 36212727 PMCID: PMC9529162 DOI: 10.1007/s00034-022-02189-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 09/12/2022] [Accepted: 09/12/2022] [Indexed: 06/16/2023]
Abstract
This paper presents a deep learning-based analysis and classification of cold speech observed when a person is diagnosed with the common cold. The common cold is a viral infectious disease that affects the throat and the nose. Since speech is produced by the vocal tract after linear filtering of excitation source information, during a common cold, its attributes are impacted by the throat and the nose. The proposed study attempts to develop a deep learning-based classification model that can accurately predict whether a person has a cold or not based on their speech. The common cold-related information is captured using Mel-frequency cepstral coefficients (MFCC) and linear predictive coding (LPC) from the speech signal. The data imbalance is handled using the sampling strategy, SMOTE-Tomek links. Then, utilizing MFCC and LPC features, a deep learning-based model is trained and then used to categorize cold speech. The performance of a deep learning-based method is compared to logistic regression, random forest, and gradient boosted tree classifiers. The proposed model is less complex and uses a smaller feature set while giving comparable results to other state-of-the-art methods. The proposed method gives an UAR of 67.71 % , higher than the benchmark OpenSMILE SVM result of 64 % . The study's success will yield a noninvasive method for cold detection, which can further be extended to detect other speech-affecting pathologies.
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Affiliation(s)
- Suman Deb
- Sardar Vallabhbhai National Institute of Technology, Surat, 395007 India
| | - Pankaj Warule
- Sardar Vallabhbhai National Institute of Technology, Surat, 395007 India
| | - Amrita Nair
- Sardar Vallabhbhai National Institute of Technology, Surat, 395007 India
| | - Haider Sultan
- Sardar Vallabhbhai National Institute of Technology, Surat, 395007 India
| | - Rahul Dash
- Sardar Vallabhbhai National Institute of Technology, Surat, 395007 India
| | - Jarek Krajewski
- Rhenish University of Applied Sciences, 50678 Cologne, Germany
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12
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Threshold prediction for detecting rare positive samples using a meta-learner. Pattern Anal Appl 2022. [DOI: 10.1007/s10044-022-01103-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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13
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Lu H, Chen H, Li T, Chen H, Luo C. Multi-label feature selection based on manifold regularization and imbalance ratio. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03141-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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14
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Cai Y, Song Y, Ni P, Liu X, Li X. Subwavelength ultrasonic imaging using a deep convolutional neural network trained on structural noise. ULTRASONICS 2021; 117:106552. [PMID: 34411873 DOI: 10.1016/j.ultras.2021.106552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 08/06/2021] [Accepted: 08/07/2021] [Indexed: 06/13/2023]
Abstract
Subwavelength ultrasonic imaging (SUI) can detect subwavelength flaws beyond the diffraction limit, however, SUI sometimes fails to clearly reveal flaws in C-scans when the signal-to-noise ratio (SNR) is low. In this work, a convolutional neural network (CNN) that takes structural noise into account is developed for SUI to distinguish flaw echoes from structural noise. The network contains a regression CNN for learning features from the structural noise and a learnable soft thresholding layer for classification. Experiments show that the proposed method performs well for imaging subwavelength flaws at different depths and of different sizes. It achieved an F1 score of 97.69 ± 1.56% in detecting flaws as compared to the enhanced ultrasonic flaw detection method with time-dependent threshold. As an example of general application of the method, we also performed SUI on natural flaws in a spheroidal graphite cast iron specimen. The results show that the method can achieve SUI without a theoretical backscattering model and is not limited by noise distribution, multiple scattering, or complex microstructures. Furthermore, the network does not need to prepare flaw echoes for training.
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Affiliation(s)
- Yongxing Cai
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China
| | - Yongfeng Song
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China.
| | - Peijun Ni
- Inner Mongolia Metallic Materials Research Institute, Ningbo 315103, China
| | - Xiling Liu
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China
| | - Xiongbing Li
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, China
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15
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Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111564. [PMID: 34770076 PMCID: PMC8583475 DOI: 10.3390/ijerph182111564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/27/2021] [Accepted: 10/30/2021] [Indexed: 11/16/2022]
Abstract
In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this paper is to propose an ordinal classification approach to predict traffic crash injury severity and to test its performance over existing machine learning classification methods. First, we compare the performance of the neural network, XGBoost, and SVM classifiers in injury severity prediction. Second, we utilize a severity category-combination method with oversampling to relieve the class-imbalance problem prevalent in crash data. Third, we take advantage of probability calibration and the optimal probability threshold moving to improve the prediction ability of ordinal classification. The proposed approach can satisfy the rank consistency and rank monotonicity requirement and is proved to be superior to other ordinal classification methods and nominal classification machine learning by statistical significance test. Important factors relating to injury severity are selected based on their permutation feature importance scores. We find that converting severity levels into three classes, minor injury, moderate injury, and serious injury, can substantially improve the prediction precision.
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16
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Miao Y, Liu Z, Wu X, Gao J. Cost-Sensitive Siamese Network for PCB Defect Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:7550670. [PMID: 34675972 PMCID: PMC8526275 DOI: 10.1155/2021/7550670] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/31/2021] [Accepted: 09/09/2021] [Indexed: 11/17/2022]
Abstract
After the production of printed circuit boards (PCB), PCB manufacturers need to remove defected boards by conducting rigorous testing, while manual inspection is time-consuming and laborious. Many PCB factories employ automatic optical inspection (AOI), but this pixel-based comparison method has a high false alarm rate, thus requiring intensive human inspection to determine whether alarms raised from it resemble true or pseudo defects. In this paper, we propose a new cost-sensitive deep learning model: cost-sensitive siamese network (CSS-Net) based on siamese network, transfer learning and threshold moving methods to distinguish between true and pseudo PCB defects as a cost-sensitive classification problem. We use optimization algorithms such as NSGA-II to determine the optimal cost-sensitive threshold. Results show that our model improves true defects prediction accuracy to 97.60%, and it maintains relatively high pseudo defect prediction accuracy, 61.24% in real-production scenario. Furthermore, our model also outperforms its state-of-the-art competitor models in other comprehensive cost-sensitive metrics, with an average of 33.32% shorter training time.
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Affiliation(s)
- Yilin Miao
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Zhewei Liu
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
| | - Xiangning Wu
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
- Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan 430078, China
| | - Jie Gao
- School of Computer Science, China University of Geosciences, Wuhan 430078, China
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Improving the phishing website detection using empirical analysis of Function Tree and its variants. Heliyon 2021; 7:e07437. [PMID: 34278030 PMCID: PMC8264617 DOI: 10.1016/j.heliyon.2021.e07437] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 04/30/2021] [Accepted: 06/25/2021] [Indexed: 11/22/2022] Open
Abstract
The phishing attack is one of the most complex threats that have put internet users and legitimate web resource owners at risk. The recent rise in the number of phishing attacks has instilled distrust in legitimate internet users, making them feel less safe even in the presence of powerful antivirus apps. Reports of a rise in financial damages as a result of phishing website attacks have caused grave concern. Several methods, including blacklists and machine learning-based models, have been proposed to combat phishing website attacks. The blacklist anti-phishing method has been faulted for failure to detect new phishing URLs due to its reliance on compiled blacklisted phishing URLs. Many ML methods for detecting phishing websites have been reported with relatively low detection accuracy and high false alarm. Hence, this research proposed a Functional Tree (FT) based meta-learning models for detecting phishing websites. That is, this study investigated improving the phishing website detection using empirical analysis of FT and its variants. The proposed models outperformed baseline classifiers, meta-learners and hybrid models that are used for phishing websites detection in existing studies. Besides, the proposed FT based meta-learners are effective for detecting legitimate and phishing websites with accuracy as high as 98.51% and a false positive rate as low as 0.015. Hence, the deployment and adoption of FT and its meta-learner variants for phishing website detection and applicable cybersecurity attacks are recommended.
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18
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Zhang L, Geisler T, Ray H, Xie Y. Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function. J Appl Stat 2021; 49:3257-3277. [DOI: 10.1080/02664763.2021.1939662] [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]
Affiliation(s)
- Lili Zhang
- Analytics and Data Science Ph.D. Program, Kennesaw State University, Kennesaw, GA, USA
| | - Trent Geisler
- Analytics and Data Science Ph.D. Program, Kennesaw State University, Kennesaw, GA, USA
| | - Herman Ray
- Analytics and Data Science Institute, Kennesaw State University, Kennesaw, GA, USA
| | - Ying Xie
- Department of Information Technology, Kennesaw State University, Kennesaw, GA, USA
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MacNeil L, Missan S, Luo J, Trappenberg T, LaRoche J. Plankton classification with high-throughput submersible holographic microscopy and transfer learning. BMC Ecol Evol 2021; 21:123. [PMID: 34134620 PMCID: PMC8207568 DOI: 10.1186/s12862-021-01839-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/25/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes-opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms-effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes. RESULTS Here we use a light and portable digital in-line holographic microscope (The HoloSea) with maximum optical resolution of 1.5 μm, intensity-based object detection through a volume, and four different pre-trained convolutional neural networks to classify > 3800 micro-mesoplankton (> 20 μm) images across 19 classes. The maximum classifier performance was quickly achieved for each convolutional neural network during training and reached F1-scores > 89%. Taking classification further, we show that off-the-shelf classifiers perform strongly across every decision threshold for ranking a majority of the plankton classes. CONCLUSION These results show compelling baselines for classifying holographic plankton images, both rare and plentiful, including several dinoflagellate and diatom groups. These results also support a broader potential for deployable holographic microscopes to sample diverse microbial eukaryotic communities, and its use for high-throughput plankton monitoring.
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Affiliation(s)
- Liam MacNeil
- Biology Department, Dalhousie University, 1355 Oxford Street, Halifax, NS, B3H 4J1, Canada.
| | - Sergey Missan
- 4Deep inwater imaging, 71 Appaloosa Run, Hammonds Plains, NS, B4B 0G2, Canada
| | - Junliang Luo
- Department of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, B3H 4R2, Canada
| | - Thomas Trappenberg
- Department of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, B3H 4R2, Canada
| | - Julie LaRoche
- Biology Department, Dalhousie University, 1355 Oxford Street, Halifax, NS, B3H 4J1, Canada.
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20
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Parghi N, Chennapragada L, Barzilay S, Newkirk S, Ahmedani B, Lok B, Galynker I. Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches. Int J Methods Psychiatr Res 2021; 30:e1863. [PMID: 33166430 PMCID: PMC7992291 DOI: 10.1002/mpr.1863] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 09/18/2020] [Accepted: 10/23/2020] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state. METHODS SCI data were collected from high-risk psychiatric inpatients (N = 591) grouped based on their short-term suicidal behavior, that is, those who attempted suicide between intake and 1-month follow-up dates (N = 20) and those who did not (N = 571). Data were analyzed using three predictive algorithms (logistic regression, random forest, and gradient boosting) and three sampling approaches (split sample, Synthetic minority oversampling technique, and enhanced bootstrap). RESULTS The enhanced bootstrap approach considerably outperformed the other sampling approaches, with random forest (98.0% precision; 33.9% recall; 71.0% Area under the precision-recall curve [AUPRC]; and 87.8% Area under the receiver operating characteristic [AUROC]) and gradient boosting (94.0% precision; 48.9% recall; 70.5% AUPRC; and 89.4% AUROC) algorithms performing best in predicting positive cases of near-term suicidal behavior using this dataset. CONCLUSIONS ML can be useful in analyzing data from psychometric scales, such as the SCI, and for predicting near-term suicidal behavior. However, in cases such as the current analysis where the data are highly imbalanced, the optimal method of measuring performance must be carefully considered and selected.
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Affiliation(s)
- Neelang Parghi
- Courant Institute of Mathematical Sciences, New York University, New York City, New York, USA
| | - Lakshmi Chennapragada
- Department of Psychiatry and Behavioral Health, Mount Sinai Beth Israel Medical Center, New York City, New York, USA
| | - Shira Barzilay
- Psychiatry Department, Schneider Children's Medical Centre, Tel Aviv University, Tel Aviv, Israel
| | - Saskia Newkirk
- Department of Psychiatry and Behavioral Health, Mount Sinai Beth Israel Medical Center, New York City, New York, USA
| | - Brian Ahmedani
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA
| | - Benjamin Lok
- College of Engineering, University of Florida, Gainesville, Florida, USA
| | - Igor Galynker
- Department of Psychiatry and Behavioral Health, Mount Sinai Beth Israel Medical Center, New York City, New York, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
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21
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Jiang G, Wang H, Peng J, Chen D, Fu X. Graph-based Multi-view Binary Learning for image clustering. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.07.132] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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22
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23
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Vuttipittayamongkol P, Elyan E, Petrovski A. On the class overlap problem in imbalanced data classification. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106631] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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24
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Zhu Y, Yan Y, Zhang Y, Zhang Y. EHSO: Evolutionary Hybrid Sampling in overlapping scenarios for imbalanced learning. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.08.060] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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25
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Namgung K, Yoon H, Baek S, Kim DY. Estimating System State through Similarity Analysis of Signal Patterns. SENSORS 2020; 20:s20236839. [PMID: 33265918 PMCID: PMC7731382 DOI: 10.3390/s20236839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/24/2020] [Accepted: 11/26/2020] [Indexed: 12/03/2022]
Abstract
State prediction is not straightforward, particularly for complex systems that cannot provide sufficient amounts of training data. In particular, it is usually difficult to analyze some signal patterns for state prediction if they were observed in both normal and fault-states with a similar frequency or if they were rarely observed in any system state. In order to estimate the system status with imbalanced state data characterized insufficient fault occurrences, this paper proposes a state prediction method that employs discrete state vectors (DSVs) for pattern extraction and then applies a naïve Bayes classifier and Brier scores to interpolate untrained pattern information by using the trained ones probabilistically. Each Brier score is transformed into a more intuitive one, termed state prediction power (SPP). The SPP values represent the reliability of the system state prediction. A state prediction power map, which visualizes the DSVs and corresponding SPP values, is provided a more intuitive way of state prediction analysis. A case study using a car engine fault simulator was conducted to generate artificial engine knocking. The proposed method was evaluated using holdout cross-validation, defining specificity and sensitivity as indicators to represent state prediction success rates for no-fault and fault states, respectively. The results show that specificity and sensitivity are very high (equal to 1) for high limit values of SPP, but drop off dramatically for lower limit values.
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Affiliation(s)
- Kichang Namgung
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Hyunsik Yoon
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Sujeong Baek
- Department of Industrial Management Engineering, Hanbat National University, Daejeon 34158, Korea
| | - Duck Young Kim
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
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26
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Adaptive Decision Threshold-Based Extreme Learning Machine for Classifying Imbalanced Multi-label Data. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10343-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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27
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Koziarski M, Woźniak M, Krawczyk B. Combined Cleaning and Resampling algorithm for multi-class imbalanced data with label noise. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106223] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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28
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Krawczyk B, Koziarski M, Wozniak M. Radial-Based Oversampling for Multiclass Imbalanced Data Classification. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:2818-2831. [PMID: 31247563 DOI: 10.1109/tnnls.2019.2913673] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Learning from imbalanced data is among the most popular topics in the contemporary machine learning. However, the vast majority of attention in this field is given to binary problems, while their much more difficult multiclass counterparts are relatively unexplored. Handling data sets with multiple skewed classes poses various challenges and calls for a better understanding of the relationship among classes. In this paper, we propose multiclass radial-based oversampling (MC-RBO), a novel data-sampling algorithm dedicated to multiclass problems. The main novelty of our method lies in using potential functions for generating artificial instances. We take into account information coming from all of the classes, contrary to existing multiclass oversampling approaches that use only minority class characteristics. The process of artificial instance generation is guided by exploring areas where the value of the mutual class distribution is very small. This way, we ensure a smart oversampling procedure that can cope with difficult data distributions and alleviate the shortcomings of existing methods. The usefulness of the MC-RBO algorithm is evaluated on the basis of extensive experimental study and backed-up with a thorough statistical analysis. Obtained results show that by taking into account information coming from all of the classes and conducting a smart oversampling, we can significantly improve the process of learning from multiclass imbalanced data.
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29
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Cheng K, Gao S, Dong W, Yang X, Wang Q, Yu H. Boosting label weighted extreme learning machine for classifying multi-label imbalanced data. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.098] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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30
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Phishing Website Detection: Forest by Penalizing Attributes Algorithm and Its Enhanced Variations. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020. [DOI: 10.1007/s13369-020-04802-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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31
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Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10072581] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.
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32
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33
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Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining. MINERALS 2020. [DOI: 10.3390/min10030257] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
The viscosity of slag is a key factor affecting metallurgical efficiency and recycling, such as metal-slag reaction and separation, as well as slag wool processing. In order to comprehensively clarify the variation of the slag viscosity, various data mining methods have been employed to predict the viscosity of the slag. In this study, a more advanced dual-stage predictive modeling approach is proposed in order to accurately analyze and predict the viscosity of slag. Compared with the traditional single data mining approach, the proposed method performs better with a higher recall rate and low misclassification rate. The simulation results show that temperature, SiO2, Al2O3, P2O5, and CaO have greater influences on the slag’s viscosity. The critical temperature for onset of the important influence of slag composition is 980 °C. Furthermore, it is found that SiO2 and P2O5 have positive correlations with slag’s viscosity, while temperature, Al2O3, and CaO have negative correlations. A two-equation model of six-degree polynomial combined with Arrhenius formula is also established for the purpose of providing theoretical guidance for industrial application and reutilization of slag.
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34
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Boosting Minority Class Prediction on Imbalanced Point Cloud Data. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10030973] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Data imbalance during the training of deep networks can cause the network to skip directly to learning minority classes. This paper presents a novel framework by which to train segmentation networks using imbalanced point cloud data. PointNet, an early deep network used for the segmentation of point cloud data, proved effective in the point-wise classification of balanced data; however, performance degraded when imbalanced data was used. The proposed approach involves removing between-class data point imbalances and guiding the network to pay more attention to majority classes. Data imbalance is alleviated using a hybrid-sampling method involving oversampling, as well as undersampling, respectively, to decrease the amount of data in majority classes and increase the amount of data in minority classes. A balanced focus loss function is also used to emphasize the minority classes through the automated assignment of costs to the various classes based on their density in the point cloud. Experiments demonstrate the effectiveness of the proposed training framework when provided a point cloud dataset pertaining to six objects. The mean intersection over union (mIoU) test accuracy results obtained using PointNet training were as follows: XYZRGB data (91%) and XYZ data (86%). The mIoU test accuracy results obtained using the proposed scheme were as follows: XYZRGB data (98%) and XYZ data (93%).
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35
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36
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Zhang L, Ray H, Priestley J, Tan S. A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data. J Appl Stat 2019; 47:568-581. [PMID: 35706966 PMCID: PMC9041569 DOI: 10.1080/02664763.2019.1643829] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions.
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Affiliation(s)
- Lili Zhang
- Analytics and Data Science Ph.D. Program, Kennesaw State University, Kennesaw, Georgia, USA
| | - Herman Ray
- Analytics and Data Science Institute, Kennesaw State University, Kennesaw, Georgia, USA
| | - Jennifer Priestley
- Analytics and Data Science Institute, Kennesaw State University, Kennesaw, Georgia, USA
| | - Soon Tan
- Ermas Consulting Inc., Alpharetta, Georgia, USA
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37
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38
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Affiliation(s)
- Hieu Pham
- Department of Industrial and Manufacturing Systems Engineering; Iowa State University; Ames Iowa
| | - Sigurdur Olafsson
- Department of Industrial and Manufacturing Systems Engineering; Iowa State University; Ames Iowa
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39
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Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest. SENSORS 2018; 18:s18041221. [PMID: 29659548 PMCID: PMC5948935 DOI: 10.3390/s18041221] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 04/10/2018] [Indexed: 11/17/2022]
Abstract
Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.
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40
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Venhuizen FG, van Ginneken B, Liefers B, van Grinsven MJ, Fauser S, Hoyng C, Theelen T, Sánchez CI. Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks. BIOMEDICAL OPTICS EXPRESS 2017; 8:3292-3316. [PMID: 28717568 PMCID: PMC5508829 DOI: 10.1364/boe.8.003292] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 05/22/2017] [Accepted: 06/03/2017] [Indexed: 05/18/2023]
Abstract
We developed a fully automated system using a convolutional neural network (CNN) for total retina segmentation in optical coherence tomography (OCT) that is robust to the presence of severe retinal pathology. A generalized U-net network architecture was introduced to include the large context needed to account for large retinal changes. The proposed algorithm outperformed qualitative and quantitatively two available algorithms. The algorithm accurately estimated macular thickness with an error of 14.0 ± 22.1 µm, substantially lower than the error obtained using the other algorithms (42.9 ± 116.0 µm and 27.1 ± 69.3 µm, respectively). These results highlighted the proposed algorithm's capability of modeling the wide variability in retinal appearance and obtained a robust and reliable retina segmentation even in severe pathological cases.
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Affiliation(s)
- Freerk G. Venhuizen
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the
Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the
Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the
Netherlands
| | - Bart Liefers
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the
Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the
Netherlands
| | - Mark J.J.P. van Grinsven
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the
Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the
Netherlands
| | - Sascha Fauser
- Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Basel,
Switzerland
- Cologne University Eye Clinic, Cologne,
Germany
| | - Carel Hoyng
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the
Netherlands
| | - Thomas Theelen
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the
Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the
Netherlands
| | - Clara I. Sánchez
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the
Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the
Netherlands
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