International Journal of Computational Intelligence Systems

Volume 8, Issue sup2, 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
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  - sup2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1129587
DO  - https://doi.org/10.1080/18756891.2015.1129587
ID  - Spolaôr2015
ER  -