Multi-label classification of documents using fine-grained weights and modified co-training

Abstract

This paper use multinomial nave Bayes to improve multi-label classification methods in a number of ways. First, we use the value weighting method, a new fine-grained weighting method, to calculate the weights of the feature values. Second, we employ a co-training method to incorporate the dependencies among the class values. The results of our experiments show that the proposed approach outperforms other state-of-the-art methods.