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A recursive PSO scheme for gene selection in microarray data
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DOI：10.1016/j.asoc.2018.06.019.png)
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Abstract
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In DNA microarray datasets, the number of genes are very large, typically in thousands while the number of samples are in hundreds. This raises the issue of generalization in the classification process. Gene selection plays a significant role in improving the accuracy. In this paper, we have proposed a recursive particle swarm optimization approach (PSO) for gene selection. The proposed method refines the feature (gene) space from a very coarse level to a fine-grained one at each recursive step of the algorithm without degrading the accuracy. In addition, we have integrated various filter based ranking methods with the proposed recursive PSO approach. We also propose to use linear support vector machine weight vector to serve as initial gene pool selection. We evaluate our method on five publicly available benchmark microarray datasets. Our approach selects only a small number of genes while yielding substantial improvements in accuracy over state-of-the-art evolutionary methods. (C) 2018 Published by Elsevier B.V.
Keywords:
Particle swarm optimization
Support vector machine (SVM)
Gene selection (GS)
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