Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.
The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combinat...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 10; pp. 1 - 10 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925180&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925180 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2015 vid: 39 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925180 115925180 115925180 10.1007/s10916-015-0306-3 115925180 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms. aug: au: Lin, Kuan-Cheng Hsieh, Yi-Hsiu affil: Department of Management Information Systems, National Chung Hsing University, 250 Kuo Kuang Rd Taichung 402 Taiwan sug: subj: Algorithms Methods Data Management Artificial Intelligence Methods Classification Particle Swarm Optimization Validity Comparative Studies Descriptive Statistics Computers and Computerization Computer Memory Escherichia Coli Parkinson Disease Cardiotocography Heart Breast Tomography, Emission-Computed, Single-Photon ab: The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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