A New Implementation of Recursive Feature Elimination Algorithm for Gene Selection from Microarray Data
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 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
Jiyang Yu, Sihua Peng, Zhizhen Wan, Xiaoping Liu, Xiaoning Peng, "A New Implementation of Recursive Feature Elimination Algorithm for Gene Selection from Microarray Data", Computer Science and Information Engineering, World Congress on, vol. 03, no. , pp. 665-669, 2009, doi:10.1109/CSIE.2009.75