Three-way decisions based multi-label learning algorithm with label dependency

Abstract

A great number of algorithms have been proposed for multilabel learning, and these algorithms usually divide the labels with an optimal threshold according to their relevances to an unseen instance. However, it may easily cause misclassification to directly determine whether an unseen instance has the label with relevance close to the threshold. The label with relevance close to the threshold has a high uncertainty. Three-way decisions theory is an efficient method to solve the uncertainty problem. Therefore, based on three-way decisions theory, a multi-label learning algorithm with label dependency is proposed in this paper. Label dependency is an inherent property in multi-label data. The labels with high uncertainty are further handled with a label dependency model, which is represented by the logistic regression in this paper. The experimental results show that this algorithm performs better.