Abstract
Several support vector machine (SVM) instances with distinct functions may be separately created and properly combined into the same learning machine structure. This is the idea underlying heterogeneous ensembles of SVMs (HE-SVMs), an approach conceived to alleviate the performance bottlenecks incurred with the kernel function choice problem inherent in SVM design. In this paper, we assess the effectiveness of applying an evolutionary based mechanism (GASe1) in the search of the optimal subset of SVM models for automatic HE-SVM construction. GASe1 has the advantage of merging both the selection and combination of component SVMs into the same optimization process, and has shown sound performance when compared with two other component selection methods in complicated classification problems.
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