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

DOI

10.1080/18756891.2015.1129587How to use a DOI?

Keywords

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

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Newton Spolaôr
AU  - Huei Diana Lee
AU  - Weber Shoity Resende Takaki
AU  - Feng Chung Wu
PY  - 2015
DA  - 2015/12/01
TI  - Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations
JO  - International Journal of Computational Intelligence Systems
SP  - 3
EP  - 15
VL  - 8
IS  - Supplement 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1129587
DO  - 10.1080/18756891.2015.1129587
ID  - Spolaôr2015
ER  -