Conditional entropy based classifier chains for multi-label classification

Abstract

In many real-world problems, data samples are simultaneously associated with multiple labels, instead of a single label. Multi-label classification deals with such problems, and has extensive applications in many fields. Among the many methods proposed for multi-label classification tasks, classifier chains (CC) is an appealing one. In the classifier chains method, the label order has a strong effect on the classification performance. However, it is difficult to determine a proper order. In this paper, we propose ordering methods based on the conditional entropy of labels. We generate a single order instead of multiple orders. Unlike existing ordering methods, there is no need to train more classifiers than CC. Experimental results on nine benchmark datasets evaluated by eight measures show that the proposed methods achieve good performance.