Minimum classification error rate training of supervised topic mixture model for multi-label text categorization

Multi-label text categorization has received more and more attention in recent years, which is more difficult but practical than the conventional binary or multi-class text categorization. Supervised topic model approaches have been proved to be effective for the multi-label text categorization. Models in most of these approaches are trained by maximum likelihood estimation (MLE). This paper proposes a discriminative learning approach based on the minimum classification error rate training to further improve the classification performance. Different properties of this method are investigated and the experimental results demonstrate the effectiveness of this new approach. The performance of the discriminative learning approach is more than 10% relatively better comparing with that of MLE training.