Dimensionality Reduction using GA-PSO
Cheng-Hong Yang 0, Chung-Jui Tu, Jun-Yang Chang, Hsiou-Hsiang Liu, Po-Chang Ko
0National Kaohsiung University of Applied Sciences
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- https://doi.org/10.2991/jcis.2006.130How 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Cheng-Hong Yang AU - Chung-Jui Tu AU - Jun-Yang Chang AU - Hsiou-Hsiang Liu AU - Po-Chang Ko PY - NaN/NaN DA - NaN/NaN TI - Dimensionality Reduction using GA-PSO BT - 9th Joint International Conference on Information Sciences (JCIS-06) PB - Atlantis Press UR - https://doi.org/10.2991/jcis.2006.130 DO - https://doi.org/10.2991/jcis.2006.130 ID - YangNaN/NaN ER -