MKBoost: A Framework of Multiple Kernel Boosting

Abstract

Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based on the proposed framework, we develop several variants of MKBoost algorithms and examine their empirical performance in comparisons to several state-of-the-art MKL algorithms on classification tasks. Experimental results show that the proposed method is more effective and efficient than the existing MKL techniques.