Stacking model of multi-label classification based on pruning strategies

Abstract

Exploiting dependencies between the labels is the key of improving the performance of multi-label classification. In this paper, we divide the utilizing methods of label dependence into two groups from the perspective of different ways of problem transformation: label grouping method and feature space extending method. As to the feature space extending method, we find that the common problem is how to measure the dependencies between labels and to select proper labels to add to the original feature space. Therefore, we propose a ReliefF-based pruning model for multi-label classification (ReliefF-based stacking, RFS). RFS measures the dependencies between labels in a feature selection perspective and then selects the more relative labels into the original feature space. Experimental results of 9 multi-label benchmark datasets shows that RFS is more effective compared to other advanced multi-label classification algorithms.