Bi-parameter space partition for cost-sensitive SVM
Bin Gu, Victor S. Sheng, Shuo Li · 2015
Model selection is an important problem of cost-sensitive SVM (CS-SVM). Although using solu-tion path to find global optimal parameters is a powerful method for model selection, it is a chal-lenge to extend the framework to solve two regu-larization parameters of CS-SVM simultaneously. To overcome this challenge, we make three main steps in this paper. (i) A critical-regions-based bi-parameter space partition algorithm is proposed to present all piecewise linearities of CS-SVM. (ii) An invariant-regions-based bi-parameter space par-tition algorithm is further proposed to compute em-pirical errors for all parameter pairs. (iii) The global optimal solutions for K-fold cross valida-tion are computed by superposing K invariant re-gion based bi-parameter space partitions into one. The three steps constitute the model selection of CS-SVM which can find global optimal parameter pairs in K-fold cross validation. Experimental re-sults on seven normal datsets and four imbalanced datasets, show that our proposed method has better generalization ability and than various kinds of grid search methods, however, with less running time.