Method of evolving non-stationary multiple kernel learning

Abstract

Recently, evolving multiple kernel learning methods have attracted researchers’ attention due to the ability to find the composite kernel with the optimal mapping model in a large high-dimensional feature space. However, it is not suitable to compute the composite kernel in a stationary way for all samples. In this paper, we propose a method of evolving non-stationary multiple kernel learning, in which base kernels are encoded as tree kernels and a gating function is used to determine the weights of the tree kernels simultaneously. Obtained classifiers have the composite kernel with the optimal mapping model and select the most appropriate combined weights according to the input samples. Experimental results on several UCI datasets illustrate the validity of proposed method.