Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations
Newton Spolaôr, Huei Lee, Weber Shoity Resende Takaki, Feng Chung Wu · International Journal of Computational Intelligence Systems · 2015
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.