International Journal of Computational Intelligence Systems

Volume 8, Issue Supplement 2, December 2015, Pages 3 - 15

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

Authors
Newton Spolaôr, Huei Diana Lee, Weber Shoity Resende Takaki, Feng Chung Wu
Corresponding Author
Newton Spolaôr
Received 29 July 2015, Accepted 30 October 2015, Available Online 1 December 2015.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
8 - 100
Pages
3 - 15
Publication Date
2015/12
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2015.1129587How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Newton Spolaôr
AU  - Huei Diana Lee
AU  - Weber Shoity Resende Takaki
AU  - Feng Chung Wu
PY  - 2015
DA  - 2015/12
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  - https://doi.org/10.1080/18756891.2015.1129587
ID  - Spolaôr2015
ER  -