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Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms

  • Systems-Level Quality Improvement
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Abstract

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.

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Authors and Affiliations

  1. Department of Management Information Systems, National Chung Hsing University, 250 Kuo Kuang Rd, Taichung, 402, Taiwan

    Kuan-Cheng Lin & Yi-Hsiu Hsieh

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  1. Kuan-Cheng Lin
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  2. Yi-Hsiu Hsieh
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Correspondence to Kuan-Cheng Lin.

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This article is part of the Topical Collection on Systems-level Quality Improvement

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Lin, KC., Hsieh, YH. Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms. J Med Syst 39, 119 (2015). https://doi.org/10.1007/s10916-015-0306-3

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  • DOI: https://doi.org/10.1007/s10916-015-0306-3

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