Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations

Abstract

Feature selection can remove non-important features from the data and promote better classifiers. This task, when applied to multi-label data where each instance is associated with a set of labels, supports emerging applications. Although multi-label data usually exhibit label relations, label dependence has been little studied in feature selection. We proposed two multi-label feature selection algorithms that consider label relations. These methods were experimentally competitive with traditional approaches. Moreover, this work conducted a systematic literature review, summarizing 74 related papers.

Keywords

• data mining
• information gain
• label construction for feature selection
• multi-label ReliefF
• machine learning
• survey