Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

Dimensionality Reduction using GA-PSO

Cheng-Hong Yang 0, Chung-Jui Tu, Jun-Yang Chang, Hsiou-Hsiang Liu, Po-Chang Ko
Corresponding Author
Cheng-Hong Yang
0National Kaohsiung University of Applied Sciences
Available Online October 2006.
DOI to use a DOI?
Feature Selection, Genetic Algorithms, Particle Swarm Optimization, K-Nearest Neighbor, Leave-one-out cross-validation.
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.
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Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
DOI to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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
PB  - Atlantis Press
SN  - 1951-6851
UR  -
DO  -
ID  - Yang2006/10
ER  -