Relative Entropy and PageRank-Based Classifier Chains for Multi-Label Classification

The Classifier Chains (CC) method is an effective method for multi-label classification, with its performance significantly contingent on the label order. However, most existing algorithms focus on determining the label order by considering label correlations as symmetric, ignoring that label correlations prevail asymmetric in real-world applications. To overcome the problem, we propose a novel Relative Entropy and PageRank-based Classifier Chains for multi-label classification (REPCC). Firstly, to completely capture label correlation, REPCC exploits relative entropy to estimate the asymmetric correlation between pairs of labels, filter out those pairs of labels with lower correlation, and construct a label correlation matrix. Secondly, by treating each label as a node, a directed graph is generated based on the filtered label correlation matrix. Finally, drawing an analogy between labels and web pages, the idea of PageRank, commonly used to measure the importance of web pages, is employed to rank labels in the directed graph and establish a classifier chain. Experimental results on ten public datasets from various domains show that REPCC outperforms state-of-the-art methods in CC.