A New Implementation of Recursive Feature Elimination Algorithm for Gene Selection from Microarray Data
Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.75
We proposed a new approach for gene selection and multi-cancer classification based on step-by-step improvement of classification performance (SSiCP).The SSiCP gene selection algorithms were evaluated over the NCI60 and GCM benchmark datasets, with an accuracy of 96.6% and 95.5% in 10-fold cross validation,respectively. Furthermore, the SSiCP outperformed recently published algorithms when applied to another two multi-cancer data sets.Computational evidence indicated that SSiCP can avoid over fitting effectively. Compared with various gene selection algorithms, the implementation of SSiCPis very simple, and all the computational experiments are repeatable.
microarray, gene expression, cancer, feature selection, machine learning
Sihua Peng, Xiaoping Liu, Jiyang Yu, Zhizhen Wan, Xiaoning Peng, "A New Implementation of Recursive Feature Elimination Algorithm for Gene Selection from Microarray Data", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 665-669, doi:10.1109/CSIE.2009.75