Multi-Label Informed Feature Selection.

Multi-label learning has been extensively studied in the area of bioinformatics, information retrieval, multimedia annotation, etc. In multi-label learning, each instance is associated with multiple interdependent class labels, the label information can be noisy and incomplete. In addition, multilabeled data often has noisy, irrelevant and redundant features of high dimensionality. As an effective data preprocessing step, feature selection has shown its effectiveness to prepare high-dimensional data for numerous data mining and machine learning tasks. Most of existing multi-label feature selection algorithms either boil down to solving multiple single-labeled feature selection problems or directly make use of imperfect labels. Therefore, they may not be able to find discriminative features that are shared by multiple labels. In this paper, we propose a novel multi-label informed feature selection framework MIFS, which exploits label correlations to select discriminative features across multiple labels. Specifically, to reduce the negative effects of imperfect label information in finding label correlations, we decompose the multi-label information into a low-dimensional space and then employ the reduced space to steer the feature selection process. Empirical studies on real-world datasets demonstrate the effectiveness and efficiency of the proposed framework.