Feature selection based on sensitivity analysis of fuzzy ISODATA

Authors:
Quanjin Liu;Zhimin Zhao;Ying-Xin Li;Yuanyuan Li
Affiliations:
College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China and School of Physics & Electronic Engineering, AnQing Normal College, Anqing, 246011, China;College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China;Institute of Machine Vision and Machine Intelligence, Beijing Jingwei Textile Machinery New Technology Co., Ltd., Beijing 100176, China;College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China
Venue:
Neurocomputing
Year:
2012

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Abstract

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.