Multi-label Classification with Meta-Labels

Abstract

The area of multi-label classification has rapidly developed in recent years. It has become widely known that the baseline binary relevance approach can easily be outperformed by methods which learn labels together. A number of methods have grown around the label power set approach, which models label combinations together as class values in a multi-class problem. We describe the label-power set-based solutions under a general framework of meta-labels and provide some theoretical justification for this framework which has been lacking, explaining how meta-labels essentially allow a random projection into a space where non-linearities can easily be tackled with established linear learning algorithms. The proposed framework enables comparison and combination of related approaches to different multi-label problems. We present a novel model in the framework and evaluate it empirically against several high-performing methods, with respect to predictive performance and scalability, on a number of datasets and evaluation metrics. This deployment obtains competitive accuracy for a fraction of the computation required by the current meta-label methods for multi-label classification.