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Classifier chain (CC) is a multi-label learning approach that constructs asequence of binary classifiers according to a label order. Each classifier inthe sequence is responsible for predicting the relevance of one label. Whentraining the classifier for a label, proceeding labels will be taken asextended features. If the extended features are highly correlated to the label,the performance will be improved, otherwise, the performance will not beinfluenced or even degraded. How to discover label correlation and determinethe label order is critical for CC approach. This paper employs Bayesiannetwork (BN) to model the label correlations and proposes a new BN-based CCmethod (BNCC). First, conditional entropy is used to describe the dependencyrelations among labels. Then, a BN is built up by taking nodes as labels andweights of edges as their dependency relations. A new scoring function isproposed to evaluate a BN structure, and a heuristic algorithm is introduced tooptimize the BN. At last, by applying topological sorting on the nodes of theoptimized BN, the label order for constructing CC model is derived.Experimental comparisons demonstrate the feasibility and effectiveness of theproposed method.

Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain