Association Rules-Based Classifier Chains Method

Abstract

The order for label learning is very important to the classifier chains method, and improper order can limit learning performance and make the model very random. Therefore, this paper proposes a classifier chains method based on the association rules (ARECC in short). ARECC first designs strong association rules based label dependence measurement strategy by combining the idea of frequent patterns; then based on label dependence relationship, a directed acyclic graph is constructed to topologically sort all vertices in the graph; next, the linear topological sequence obtained is used as the learning order of labels to train each label’s classifier; finally, ARECC uses association rules to modify and update the probability of the prediction for each label. By mining the label dependencies, ARECC writes the correlation information between labels in the topological sequence, which improves the utilization of the correlation information. Experimental results of a variety of public multi-label datasets show that ARECC can effectively improve classification performance.

Keywords

• Multi-label learning
• classifier chains
• label correlations
• association rules