Multi-labelled Classification Using Maximum Entropy Method

Abstract

Many classification problems require classifiers to assign each single document into more than one category, which is called <i>multi-labelled classification</i>. The categories in such problems usually are neither conditionally independent from each other nor mutually exclusive, therefore it is not trivial to directly employ state-of-the-art classification algorithms without losing information of relation among categories. In this paper, we explore correlations among categories with maximum entropy method and derive a classification algorithm for multi-labelled documents. Our experiments show that this method significantly outperforms the combination of single label approach.