Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis |
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| Affiliation: | 1. Department of Applied Mathematics and Computer Science, Ghent University, Belgium;2. Affectv Limited, London, United Kingdom;3. Department of Computer Science and AI, Research Center on Information and Communications Technology (CITIC-UGR), University of Granada, Spain;4. Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia;1. Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, United States;2. Institute for Energy Technology (IFE), Department of Process and Fluid Flow Technology, 2027 Kjeller, Norway;3. École Centrale de Lille, Université Lille Nord de France, 59655 Villeneuve d’Ascq, France |
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| Abstract: | 
One of the most powerful, popular and accurate classification techniques is support vector machines (SVMs). In this work, we want to evaluate whether the accuracy of SVMs can be further improved using training set selection (TSS), where only a subset of training instances is used to build the SVM model. By contrast to existing approaches, we focus on wrapper TSS techniques, where candidate subsets of training instances are evaluated using the SVM training accuracy. We consider five wrapper TSS strategies and show that those based on evolutionary approaches can significantly improve the accuracy of SVMs. |
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| Keywords: | Support vector machines Training set selection Data reduction |
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