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Dang Q, Gao W, Gong M. An efficient mixture sampling model for gaussian estimation of distribution algorithm. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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2
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Comparison of Selection Criteria for Model Selection of Support Vector Machine on Physiological Data with Inter-Subject Variance. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031749] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Support vector machines (SVMs) utilize hyper-parameters for classification. Model selection (MS) is an essential step in the construction of the SVM classifier as it involves the identification of the appropriate parameters. Several selection criteria have been proposed for MS, but their usefulness is limited for physiological data exhibiting inter-subject variance (ISV) that makes different characteristics between training and test data. To identify an effective solution for the constraint, this study considered a leave-one-subject-out cross validation-based selection criterion (LSSC) with six well-known selection criteria and compared their effectiveness. Nine classification problems were examined for the comparison, and the MS results of each selection criterion were obtained and analyzed. The results showed that the SVM model selected by the LSSC yielded the highest average classification accuracy among all selection criteria in the nine problems. The average accuracy was 2.96% higher than that obtained with the conventional K-fold cross validation-based selection criterion. In addition, the advantage of the LSSC was more evident for data with larger ISV. Thus, the results of this study can help optimize SVM classifiers for physiological data and are expected to be useful for the analysis of physiological data to develop various medical decision systems.
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Díaz-Pacheco A, Reyes-Garcia CA. A classification-based fuzzy-rules proxy model to assist in the full model selection problem in high volume datasets. J EXP THEOR ARTIF IN 2021. [DOI: 10.1080/0952813x.2021.1925972] [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)
- Angel Díaz-Pacheco
- Computer Science Department, Instituto Nacional de Astrofísica, Óptica Y Electrónica (INAOE), Puebla, Mexico
| | - Carlos Alberto Reyes-Garcia
- Computer Science Department, Instituto Nacional de Astrofísica, Óptica Y Electrónica (INAOE), Puebla, Mexico
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How to tune the RBF SVM hyperparameters? An empirical evaluation of 18 search algorithms. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10011-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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5
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Hsia JY, Lin CJ. Parameter Selection for Linear Support Vector Regression. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:5639-5644. [PMID: 32071005 DOI: 10.1109/tnnls.2020.2967637] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In linear support vector regression (SVR), the regularization and error sensitivity parameters are used to avoid overfitting the training data. A proper selection of parameters is very essential for obtaining a good model, but the search process may be complicated and time-consuming. In an earlier work by Chu et al. (2015), an effective parameter-selection procedure by using warm-start techniques to solve a sequence of optimization problems has been proposed for linear classification. We extend their techniques to linear SVR, but address some new and challenging issues. In particular, linear classification involves only the regularization parameter, but linear SVR has an extra error sensitivity parameter. We investigate the effective range of each parameter and the sequence in checking the two parameters. Based on this work, an effective tool for the selection of parameters for linear SVR has been available for public use.
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Application of incremental support vector regression based on optimal training subset and improved particle swarm optimization algorithm in real-time sensor fault diagnosis. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01916-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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7
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Xie X, Chen C, Sun T, Mamati G, Wan X, Zhang W, Gao R, Chen F, Wu W, Fan Y, Lv X, Wu G. Rapid, non-invasive screening of keratitis based on Raman spectroscopy combined with multivariate statistical analysis. Photodiagnosis Photodyn Ther 2020; 31:101932. [PMID: 32717454 DOI: 10.1016/j.pdpdt.2020.101932] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 06/29/2020] [Accepted: 07/17/2020] [Indexed: 02/07/2023]
Abstract
This study proposes a multivariate statistical analysis method based on Raman spectroscopy and different dimensionality reduction methods combined with the support vector machine (SVM) algorithm for rapid, non-invasive, high-accuracy classification of keratitis screenings. In this experiment, tear samples from 19 subjects with keratitis and 27 healthy subjects were detected, Raman spectra of the two groups of subjects were compared and analysed, and we found that their spectral intensities were different at 1005 cm-1 and 1155 cm-1 Principal component analysis (PCA) and partial least squares (PLS) were used for feature extraction, which greatly reduced the dimensionality of the high-dimensional spectral data. Then, the above two feature extraction methods were used as input to an SVM to build the discriminant diagnosis model. The average accuracy obtained from the PCA-SVM and PLS-SVM models was 77.86 % and 100 %, respectively. Our results suggest that tear Raman spectroscopy combined with multivariate statistical analysis has great potential in screening for keratitis. We expect this technology to could lead to the development of a portable, non-invasive and highly accurate keratitis screening device.
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Affiliation(s)
- Xiaodong Xie
- People's Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Ophthalmology, Urumqi 830001, China
| | - Cheng Chen
- School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Tiantian Sun
- School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Gulinur Mamati
- People's Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Ophthalmology, Urumqi 830001, China
| | - Xinjuan Wan
- People's Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Ophthalmology, Urumqi 830001, China
| | - Wenjuan Zhang
- People's Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Ophthalmology, Urumqi 830001, China
| | - Rui Gao
- School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Fangfang Chen
- School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
| | - Wei Wu
- School of Software, Xinjiang University, Urumqi 840046, China
| | - Yangyang Fan
- School of Software, Xinjiang University, Urumqi 840046, China
| | - Xiaoyi Lv
- School of Software, Xinjiang University, Urumqi 840046, China.
| | - Guohua Wu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
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Allegretta I, Marangoni B, Manzari P, Porfido C, Terzano R, De Pascale O, Senesi GS. Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms. Talanta 2020; 212:120785. [PMID: 32113548 DOI: 10.1016/j.talanta.2020.120785] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 01/21/2020] [Accepted: 01/25/2020] [Indexed: 11/16/2022]
Abstract
The research on meteorites from hot and cold deserts is gaining advantages from the recent improvements of portable technologies such as X-ray fluorescence spectroscopy (XRF). The main advantages of portable instruments include the fast recognition of meteorites through their classification in macro-groups and discrimination from materials such as industrial slags, desert varnish covered rocks and iron oxides, named "meteor-wrongs". In this study, 18 meteorite samples of different nature and origin were discriminated and preliminarily classified into characteristic macro-groups: iron meteorites, stony meteorites and meteor-wrongs, combining a portable energy dispersive XRF instrument (pED-XRF), principal component analysis (PCA) and some machine learning algorithms applied to the XRF spectra. The results showed that 100% accuracy in sample classification was obtained by applying the cubic support vector machine (CSVM), fine kernel nearest neighbor (FKNN), subspace discriminant-ensemble classifiers (SD-EC) and subspace discriminant KNN-EC (SKNN-EC) algorithms on standardized spectra.
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Affiliation(s)
- Ignazio Allegretta
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari "Aldo Moro", Via Amendola 165/A, 70126, Bari, Italy
| | - Bruno Marangoni
- Physics Institute, Federal University of Mato Grosso do Sul, P.O. Box 549, Campo Grande, MS, 79070-900, Brazil
| | - Paola Manzari
- Agenzia Spaziale Italiana, via del Politecnico, 00133, Roma, Italy
| | - Carlo Porfido
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari "Aldo Moro", Via Amendola 165/A, 70126, Bari, Italy
| | - Roberto Terzano
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari "Aldo Moro", Via Amendola 165/A, 70126, Bari, Italy
| | - Olga De Pascale
- CNR - Istituto per la Scienza e Tecnologia dei Plasmi (ISTP) - Sede di Bari, Via Amendola 122/D, 70126, Bari, Italy
| | - Giorgio S Senesi
- CNR - Istituto per la Scienza e Tecnologia dei Plasmi (ISTP) - Sede di Bari, Via Amendola 122/D, 70126, Bari, Italy.
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9
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10
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Exploring spatial–temporal relations via deep convolutional neural networks for traffic flow prediction with incomplete data. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.09.040] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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11
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12
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Pischel D, Buchbinder JH, Sundmacher K, Lavrik IN, Flassig RJ. A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data. PLoS One 2018; 13:e0197208. [PMID: 29768460 PMCID: PMC5955558 DOI: 10.1371/journal.pone.0197208] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 04/27/2018] [Indexed: 11/24/2022] Open
Abstract
Imaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between different cellular phenotypes such as proliferation, cell division and cell death. In the course of undergoing these different pathways, each cell is characterized by a high amount of properties. This makes it hard to filter the most relevant information for cell state discrimination. The traditional methods for cell state discrimination rely on dye based two-dimensional gating strategies ignoring information that is hidden in the high-dimensional property space. In order to make use of the information ignored by the traditional methods, we present a simple and efficient approach to distinguish biological states within a cell population based on machine learning techniques. We demonstrate the advantages and drawbacks of filter techniques combined with different classification schemes. These techniques are illustrated with two case studies of apoptosis detection in HeLa cells. Thereby we highlight (i) the aptitude of imaging flow cytometry regarding automated, label-free cell state discrimination and (ii) pitfalls that are frequently encountered. Additionally a MATLAB script is provided, which gives further insight regarding the computational work presented in this study.
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Affiliation(s)
- Dennis Pischel
- Process Systems Engineering, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Jörn H. Buchbinder
- Translational Inflammation Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Kai Sundmacher
- Process Systems Engineering, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Inna N. Lavrik
- Translational Inflammation Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Robert J. Flassig
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
- * E-mail:
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Parzen neural networks: Fundamentals, properties, and an application to forensic anthropology. Neural Netw 2017; 97:137-151. [PMID: 29096202 DOI: 10.1016/j.neunet.2017.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 09/27/2017] [Accepted: 10/05/2017] [Indexed: 11/21/2022]
Abstract
A novel, unsupervised nonparametric model of multivariate probability density functions (pdf) is introduced, namely the Parzen neural network (PNN). The PNN is intended to overcome the major limitations of traditional (either statistical or neural) pdf estimation techniques. Besides being profitably simple, the PNN turns out to have nice properties in terms of unbiased modeling capability, asymptotic convergence, and efficiency at test time. Several matters pertaining the practical application of the PNN are faced in the paper, too. Experiments are reported, involving (i) synthetic datasets, and (ii) a challenging sex determination task from 1400 scout-view CT-scan images of human crania. Incidentally, the empirical evidence entails also some conclusions of high anthropological relevance.
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Chen Y, Sha M, Zhao X, Ma J, Ni H, Gao W, Ming D. Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method. Psychiatry Res Neuroimaging 2017; 264:35-45. [PMID: 28448817 DOI: 10.1016/j.pscychresns.2017.04.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 04/01/2017] [Accepted: 04/12/2017] [Indexed: 10/19/2022]
Abstract
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however, the indistinct relationship between diffusivity and kurtosis has not been clearly revealed in clinical settings. In this study, we hypothesize that the combination of diffusivity and kurtosis in DKI improves the capacity of DKI to detect Alzheimer's disease compared with diffusivity or kurtosis alone. Specifically, a support vector machine-based approach was applied to combine diffusivity and kurtosis and to compare different indices datasets. Strict assessments were conducted to ensure the reliability of all classifiers. Then, data from the optimized classifiers were used to detect abnormalities. With the combination, high accuracy performances of 96.23% were obtained in 53 subjects, including 27 Alzheimer's patients. More highly scored abnormal regions were selected by the combination than alone. The results revealed that more precise diffusivity and complementary kurtosis mainly contributed to the high performance of the combination in DKI. This study provides further understanding of DKI and the relationship between diffusivity and kurtosis in pathologic white matter alterations in Alzheimer's disease.
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Affiliation(s)
- Yuanyuan Chen
- School of Electronics and Information Engineering, Tianjin University, Tianjin, China.
| | - Miao Sha
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
| | - Xin Zhao
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
| | - Jianguo Ma
- School of Electronics and Information Engineering, Tianjin University, Tianjin, China.
| | - Hongyan Ni
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China.
| | - Wei Gao
- Department of Biomedical Sciences and Academic Imaging, Cedars-Sinai Medical Center, CA, USA.
| | - Dong Ming
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
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16
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Che J, Yang Y, Li L, Li Y, Zhu S. A modified support vector regression: Integrated selection of training subset and model. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.12.053] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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17
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A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-2930-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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18
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Razzaghi T, Roderick O, Safro I, Marko N. Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values. PLoS One 2016; 11:e0155119. [PMID: 27195952 PMCID: PMC4873242 DOI: 10.1371/journal.pone.0155119] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Accepted: 04/25/2016] [Indexed: 11/19/2022] Open
Abstract
This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.
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Affiliation(s)
- Talayeh Razzaghi
- School of Computing, Clemson University, Clemson, SC, United States of America
| | - Oleg Roderick
- Department of Data Science, Geisinger Health System, Danville, PA, United States of America
| | - Ilya Safro
- School of Computing, Clemson University, Clemson, SC, United States of America
- * E-mail:
| | - Nicholas Marko
- Department of Data Science, Geisinger Health System, Danville, PA, United States of America
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19
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Facial neuromuscular signal classification by means of least square support vector machine for MuCI. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.01.034] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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20
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Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.08.010] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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21
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Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system. ScientificWorldJournal 2014; 2014:137896. [PMID: 25295291 PMCID: PMC4177776 DOI: 10.1155/2014/137896] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Revised: 08/07/2014] [Accepted: 08/20/2014] [Indexed: 11/17/2022] Open
Abstract
The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.
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Cortez P, Embrechts MJ. Using sensitivity analysis and visualization techniques to open black box data mining models. Inf Sci (N Y) 2013. [DOI: 10.1016/j.ins.2012.10.039] [Citation(s) in RCA: 159] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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24
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Mao W, Mu X, Zheng Y, Yan G. Leave-one-out cross-validation-based model selection for multi-input multi-output support vector machine. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-1234-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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25
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Kapp MN, Sabourin R, Maupin P. A dynamic model selection strategy for support vector machine classifiers. Appl Soft Comput 2012. [DOI: 10.1016/j.asoc.2012.04.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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26
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Learning Rates for Regularized Classifiers Using Trigonometric Polynomial Kernels. Neural Process Lett 2012. [DOI: 10.1007/s11063-012-9217-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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27
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Zou T, Cai M, Du R, Liu J. Analyzing the uncertainty of simulation results in accident reconstruction with Response Surface Methodology. Forensic Sci Int 2012; 216:49-60. [DOI: 10.1016/j.forsciint.2011.08.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2011] [Revised: 08/13/2011] [Accepted: 08/18/2011] [Indexed: 10/17/2022]
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28
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Chang CC, Pao HK, Lee YJ. An RSVM based two-teachers–one-student semi-supervised learning algorithm. Neural Netw 2012; 25:57-69. [DOI: 10.1016/j.neunet.2011.06.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2010] [Revised: 02/17/2011] [Accepted: 06/27/2011] [Indexed: 10/18/2022]
Affiliation(s)
- Chien-Chung Chang
- Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan.
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Tovar D, Cornejo E, Xanthopoulos P, Guarracino MR, Pardalos PM. Data mining in psychiatric research. Methods Mol Biol 2012; 829:593-603. [PMID: 22231840 DOI: 10.1007/978-1-61779-458-2_37] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Mathematical sciences and computational methods have found new applications in fields like medicine over the last few decades. Modern data acquisition and data analysis protocols have been of great assistance to medical researchers and clinical scientists. Especially in psychiatry, technology and science have made new computational methods available to assist the development of predictive modeling and to identify diseases more accurately. Data mining (or knowledge discovery) aims to extract information from large datasets and solve challenging tasks, like patient assessment, early mental disease diagnosis, and drug efficacy assessment. Accurate and fast data analysis methods are very important, especially when dealing with severe psychiatric diseases like schizophrenia. In this paper, we focus on computational methods related to data analysis and more specifically to data mining. Then, we discuss some related research in the field of psychiatry.
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Affiliation(s)
- Diego Tovar
- Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida, Gainesville, FL, USA
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De Brabanter K, De Brabanter J, Suykens J, De Moor B. Optimized fixed-size kernel models for large data sets. Comput Stat Data Anal 2010. [DOI: 10.1016/j.csda.2010.01.024] [Citation(s) in RCA: 81] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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31
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32
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Uniform design over general input domains with applications to target region estimation in computer experiments. Comput Stat Data Anal 2010. [DOI: 10.1016/j.csda.2009.08.008] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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33
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34
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Nonlinear measures of association with kernel canonical correlation analysis and applications. J Stat Plan Inference 2009. [DOI: 10.1016/j.jspi.2008.10.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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35
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Chen PC, Huang SY, Chen WJ, Hsiao CK. A new regularized least squares support vector regression for gene selection. BMC Bioinformatics 2009; 10:44. [PMID: 19187562 PMCID: PMC2669483 DOI: 10.1186/1471-2105-10-44] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2008] [Accepted: 02/03/2009] [Indexed: 11/28/2022] Open
Abstract
Background Selection of influential genes with microarray data often faces the difficulties of a large number of genes and a relatively small group of subjects. In addition to the curse of dimensionality, many gene selection methods weight the contribution from each individual subject equally. This equal-contribution assumption cannot account for the possible dependence among subjects who associate similarly to the disease, and may restrict the selection of influential genes. Results A novel approach to gene selection is proposed based on kernel similarities and kernel weights. We do not assume uniformity for subject contribution. Weights are calculated via regularized least squares support vector regression (RLS-SVR) of class levels on kernel similarities and are used to weight subject contribution. The cumulative sum of weighted expression levels are next ranked to select responsible genes. These procedures also work for multiclass classification. We demonstrate this algorithm on acute leukemia, colon cancer, small, round blue cell tumors of childhood, breast cancer, and lung cancer studies, using kernel Fisher discriminant analysis and support vector machines as classifiers. Other procedures are compared as well. Conclusion This approach is easy to implement and fast in computation for both binary and multiclass problems. The gene set provided by the RLS-SVR weight-based approach contains a less number of genes, and achieves a higher accuracy than other procedures.
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Affiliation(s)
- Pei-Chun Chen
- 1Bioinformatics and Biostatistics Core Laboratory, National Taiwan University, Taipei, Taiwan, Republic of China.
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Croux C, Gallopoulos E, Van Aelst S, Zha H. Machine Learning and Robust Data Mining. Comput Stat Data Anal 2007. [DOI: 10.1016/j.csda.2007.06.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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