Abstract
Finding a kernel mapping function is a key step towards construction of a high-performanced SVM-based classifier. While some recent methods exploited an evolutional approach to construct a suitable multifunction kernel, most of them searched randomly and diversely. In this paper, the concept of a family of identical-structured kernel trees is proposed to enable exploration of structure space using genetic programming whereas to pursue investigation of parameter space on a certain tree using evolutional strategy. To control balance between structure and parameter search towards an optimal kernel, the trade-off strategy is introduced. By experiments on a number of benchmark datasets from UCI and text classification datasets, the proposed method is shown to be able to find a better optimal solution than other search methods.
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Methasate, I., Theeramunkong, T. (2009). A Family-Based Evolutional Approach for Kernel Tree Selection in SVMs. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_111
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DOI: https://doi.org/10.1007/978-3-642-01307-2_111
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