Improving Multilabel Classification Performance by Using Ensemble of Multi-label Classifiers

Multilabel classification is a challenging research problem in which each instance is assigned to a subset of labels. Recently, a considerable amount of research has been concerned with the development of “good” multi-label learning methods. Despite the extensive research effort, many scientific challenges posed by e.g. highly imbalanced training sets and correlation among labels remain to be addressed. The aim of this paper is use heterogeneous ensemble of multi-label learners to simultaneously tackle both imbalance and correlation problems. This is different from the existing work in the sense that the later mainly focuses on ensemble techniques within a multi-label learner while we are proposing in this paper to combine these state-of-the-art multi-label methods by ensemble techniques. The proposed ensemble approach (EML) is applied to three publicly available multi-label data sets using several evaluation criteria. We validate the advocated approach experimentally and demonstrate that it yields significant performance gains when compared with state-of-the art multi-label methods.