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

Author(s)

Spolaor, Newton ; Lee, Huei Diana ; Resende Takaki, Weber Shoity ; Wu, Feng Chung

• Document type: Journal article
• Source: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS; v. 8, p. 13-pg., 2015-12-11.

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. (AU)

• FAPESP's process: 11/02393-4 - Feature Selection for Multi-label Learning
• Grantee: Newton Spolaôr
• Support Opportunities: Scholarships in Brazil - Doctorate