Volume 8, Issue Supplement 2, December 2015, Pages 3 - 15
Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations
Newton Spolaôr, Huei Diana Lee, Weber Shoity Resende Takaki, Feng Chung Wu
Received 29 July 2015, Accepted 30 October 2015, Available Online 1 December 2015.
- https://doi.org/10.1080/18756891.2015.1129587How to use a DOI?
- data mining, information gain, label construction for feature selection, multi-label ReliefF, machine learning, survey
- 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.
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 -