Efficient text categorization using a min-max modular support vector machine

Abstract

The min-max modular support vector machine (M3-SVM) has been proposed for solving large-scale and complex multiclass classification problems. In this paper, we apply the M3-SVM to multilabel text categorization and introduce two task decomposition strategies into M3-SVMs. A multilabel classification task can be split up into a set of two-class classification tasks. These two-class tasks are to discriminate class C from non-class C. If these two class tasks are still hard to be learned, we can further divide them into a set of two-class tasks as small as needed and fast training of SVMs on massive multilabel texts can be easily implemented in a massively parallel way. Furthermore, we proposed a new task decomposition strategy called hyperplane task decomposition to improve generalization performance. The experimental results indicate that the new method has better generalization performance than traditional SVMs and previous M3-SVMs using random task decomposition, and is much faster than traditional SVMs.