Title :
Gene selection and classification by entropy-based recursive feature elimination
Author :
Furlanello, C. ; Serafini, M. ; Merler, S. ; Jurman, G.
Author_Institution :
ITC-irst, Trento, Italy
Abstract :
We analyse E-RFE (entropy-based recursive feature elimination), a new wrapper algorithm for fast feature ranking in classification problems. The E-RFE method operates the elimination of chunks of uninteresting features according to the entropy of the weights distribution of a SVM classifier. The method is designed to support computationally intensive model selection in classification problems in which the number of features is much larger than the number of samples. We test the elimination procedure on synthetic data sets, and we demonstrate the applicability of E-RFE for the identification of biomedically important genes in predictive classification of microarray data.
Keywords :
entropy; genetics; patient diagnosis; pattern classification; support vector machines; SVM classifier; biomedically important genes identification; computationally intensive model selection; entropy-based recursive feature elimination; feature ranking; gene classification; gene selection; microarray data; predictive classification; weights distribution; wrapper algorithm; Acceleration; Algorithm design and analysis; Biomedical computing; Design methodology; Diseases; Entropy; Gene expression; Predictive models; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1224063
