Dimensionality Reduction using GA-PSO

DOI

10.2991/jcis.2006.130How to use a DOI?

Keywords

Feature Selection, Genetic Algorithms, Particle Swarm Optimization, K-Nearest Neighbor, Leave-one-out cross-validation.

Abstract

The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable classification accuracy. In this paper, we propose a combination of genetic algorithms (GAs) and particle swarm optimization (PSO) for feature selection. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as an evaluator for the GAs and the PSO. The proposed method is applied to five classification problems taken from the literature. Experimental results show that our method simplifies features effectively and obtains a higher classification accuracy compared to other feature selection methods.

Copyright

© 2006, 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  - CONF
AU  - Cheng-Hong Yang
AU  - Chung-Jui Tu
AU  - Jun-Yang Chang
AU  - Hsiou-Hsiang Liu
AU  - Po-Chang Ko
PY  - 2006/10
DA  - 2006/10
TI  - Dimensionality Reduction using GA-PSO
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.130
DO  - 10.2991/jcis.2006.130
ID  - Yang2006/10
ER  -