A Binary Krill Herd Approach for Feature Selection
Loading...
External sources
External sources
Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Ieee Computer Soc
Institute of Electrical and Electronics Engineers (IEEE)
Institute of Electrical and Electronics Engineers (IEEE)
Type
Work presented at event
Access right
Acesso aberto
External sources
External sources
Abstract
Meta-heuristic-based feature selection has been paramount in the last years, mainly because of its simplicity, effectiveness and also efficiency in some cases. Such approaches are based on the social dynamics of living organisms, and can vary from birds, bees, bats and ants. Very recently, an optimization algorithm based on krill herd (KH) was proposed for continuous-valued applications, and it has been more accurate than some state-of-the-art techniques. In this paper, we propose a binary optimization version of KH technique, and we validate it for feature selection purposes in several datasets. The experiments showed the proposed technique outperforms three other meta-heuristic-based approaches for this task, being also so fast as the compared techniques.
Description
Keywords
Language
English
Citation
2014 22nd International Conference On Pattern Recognition (icpr). Los Alamitos: Ieee Computer Soc, p. 1407-1412, 2014.
