A Family-Based Evolutional Approach for Kernel Tree Selection in SVMs

作者：Methasate Ithipan^*; Theeramunkong Thanaruk

来源：IEICE Transactions on Information and Systems, 2010, E93D(4): 909-921.

DOI：10.1587/transinf.E93.D.909

• support vector machines
• multifunction kernel
• kernel tree
• optimal search
• simulated annealing

Finding a kernel mapping function for support vector machines (SVMs) 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 evolution strategy To control balance between structure and parameter search towards an optimal kernel, simulated aline:ding is introduced By experiments on a number of benchmark datasets in the UCI and text classification collection the proposed method is shown to be able to find a better optimal solution than other search methods, including grid search and gradient search.

• 出版日期2010-4