MLRF: Multi-label Classification Through Random Forest with Label-Set Partition

Abstract

Although random forest is one of the best ensemble learning algorithms for single-label classification, exploiting it for multi-label classification problems is still challenging and few method has been investigated in the literature. This paper proposes MLRF, a multi-label classification method based on a variation of random forest. In this algorithm, a new label set partition method is proposed to transform multi-label data sets into multiple single-label data sets, which can effectively discover correlated labels to optimize the label subset partition. For each generated single-label subset, a random forest classifier is learned by an improved random forest algorithm that employs a kNN-like on-line instance sampling method. Experimental results on ten benchmark data sets have demonstrated that MLRF outperforms other state-of-the-art multi-label classification algorithms in terms of classification performance as well as various evaluation criteria widely used for multi-label classification.