Chen, Hsin-Hua; Pai, Ping-Feng; Cho, Ying-Zhieh; Lee, Fong-Chuan; Fu, Ja-Chih An improved support vector machines model in medical data analysis. (English) Zbl 1184.62103 Int. J. Math. Model. Numer. Optim. 1, No. 3, 168-184 (2010). Summary: The support vector machine (SVM) technique is an emerging classification scheme that has been successfully employed in solving many classification problems. However, three main traits: features selection, dimension reduction and parameters selection, essentially influence the classification performance of SVM models. Therefore, this study developed an improved support vector machine (IMSVM) model using factor analysis (FA), kernel sliced inverse regression (KSIR) and honey-bee mating optimisation with genetic algorithms (HBMOG) to deal with feature selection, dimension reduction, and parameter selection issues, respectively, for SVM models. Then, the statlog heart data set from the Center for Machine Learning and Intelligent Systems at the University of California, Irvine (UCI) was used to demonstrate the performance of the IMSVM model. Experimental results revealed that the IMSVM model can provide more accurate classification results than the results obtained by classification models in previous literature. Thus, the proposed model is a promising alternative for analysing medical data. MSC: 62H30 Classification and discrimination; cluster analysis (statistical aspects) 62H25 Factor analysis and principal components; correspondence analysis 90C59 Approximation methods and heuristics in mathematical programming 68T05 Learning and adaptive systems in artificial intelligence Keywords:support vector machines; SVM; factor analysis; honey bee mating optimisation; genetic algorithms; kernel sliced inverse regression; medical data analysis; classification; feature selection; dimension reduction; parameter selection PDFBibTeX XMLCite \textit{H.-H. Chen} et al., Int. J. Math. Model. Numer. Optim. 1, No. 3, 168--184 (2010; Zbl 1184.62103) Full Text: DOI