Improving Multi-Label Classification by Means of Cross-Ontology Association Rules

Abstract

Recently several methods were proposed for the improvement of multi-label classi cation performance by using constraints on labels. Such constraints are based on dependencies between classes often present in multi-label data and can be mined as association rules from training data. The rules are then applied in a post-processing step to correct the classi er predictions. Due to properties of association rule mining these improvement methods often achieve low improvement expressed mostly in the better prediction of large classes. In the presence of class ontologies this is undesirable because larger classes correspond to higher levels in hierarchies presenting general concepts and can thus be trivial. In this paper we overcome the problem by focusing on improving multi-label classi cation performance on small classes. We present a new method of improvement based on mining cross-ontology association rules which is best suited for classi cation with multiple class ontologies, but can also be applied to multi-label classi cation with one class taxonomy.