A hybrid feature selection method for DNA microarray data

作者：Chuang Li Yeh; Yang Cheng Huei; Wu Kuo Chuan; Yang Cheng Hong

来源：Computers in Biology and Medicine, 2011, 41(4): 228-237.

DOI：10.1016/j.compbiomed.2011.02.004

• Feature selection
• Taguchi-genetic algorithm
• K-nearest neighbor
• Leave-one-out cross-validation

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature.

• 出版日期2011-4