Fast text categorization with min-max modular support vector machines

The min-max modular support vector machines (M³-SVMs) have been proposed for solving large-scale and complex multiclass classification problems. In this paper, we apply the M³-SVMs to multilabel text categorization and introduce a new task decomposition strategy 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 the C class from non-C class. 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 on the RC 1-v2 indicate that the new method has better generalization performance than traditional SVMs and previous M³-SVMs using random task decomposition, and is faster than traditional SVMs.