Multi-label classification systems by the use of supervised clustering

Abstract

Multi-label classification problem involves finding a model that maps a set of input features to more than one output labels. It is well known that, exploiting label correlations is important for multi-label learning. In this paper, a supervised clustering-based multi-label classification method is proposed that uses supervised clustering for considering label correlations. The proposed approach enhanced the performance of multi-label classification systems in comparison with the state of the art. Experimental results on a number of image, music and text datasets validate the effectiveness of the proposed approach.