Feature selection based on sensitivity analysis of fuzzy ISODATA

A feature selection method based on sensitivity analysis and the fuzzy Interactive Self-Organizing Data Algorithm (ISODATA) is proposed for selecting features from high dimensional gene expression data sets. First, feature sensitivities for discriminating classes are calculated on the basis of the fuzzy ISODATA method. Then, candidate feature subsets are generated according to feature sensitivities with the recursive feature elimination procedure. Finally, the obtained optimal feature subsets are evaluated using both supervised and unsupervised methods to validate their abilities for separating different categories. The proposed method is applied to five microarray datasets, and the experimental results indicate its effectiveness.