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JACIII Vol.12 No.3 pp. 218-226
doi: 10.20965/jaciii.2008.p0218
(2008)

Paper:

A Combination of Shuffled Frog-Leaping Algorithm and Genetic Algorithm for Gene Selection

Cheng-San Yang*, Li-Yeh Chuang**, Chao-Hsuan Ke***,
and Cheng-Hong Yang***

*Institute of biomedical engineering, National Cheng Kung University, Tainan, Taiwan 70101

**Department of Chemical Engineering, I-Shou University, Kaohsiung, Taiwan 84001

***Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan 80778

Received:
April 20, 2007
Accepted:
September 22, 2007
Published:
May 20, 2008
Keywords:
gene expression data, classification, SFLA, GA, KNN
Abstract

Microarray data referencing to gene expression profiles provides valuable answers to a variety of problems, and contributes to advances in clinical medicine. The application of microarray data to the classification of cancer types has recently assumed increasing importance. The classification of microarray data samples involves feature selection, whose goal is to identify subsets of differentially expressed gene potentially relevant for distinguishing sample classes and classifier design. We propose an efficient evolutionary approach for selecting gene subsets from gene expression data that effectively achieves higher accuracy for classification problems. Our proposal combines a shuffled frog-leaping algorithm (SFLA) and a genetic algorithm (GA), and chooses genes (features) related to classification. The K-nearest neighbor (KNN) with leave-one-out cross validation (LOOCV) is used to evaluate classification accuracy. We apply a novel hybrid approach based on SFLA-GA and KNN classification and compare 11 classification problems from the literature. Experimental results show that classification accuracy obtained using selected features was higher than the accuracy of datasets without feature selection.

Cite this article as:
C. Yang, L. Chuang, C. Ke, and <. Yang, “A Combination of Shuffled Frog-Leaping Algorithm and Genetic Algorithm for Gene Selection,” J. Adv. Comput. Intell. Intell. Inform., Vol.12, No.3, pp. 218-226, 2008.
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