A Hybrid Feature Selection Method Using Gene Expression Data

In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined in a hybrid method, and the K-nearest neighbor (KNN) method with leave-one-out cross-validation (LOOCV) served as a classifier for eleven classification profiles. With the help of this classifier classification accuracy were calculated. Experimental results show that this method effectively simplifies features selection by reducing the total number of features needed. The proposed method obtained the highest classification accuracy in five out of the six gene expression data set test problems when compared to other classification methods from the literature.