Gene and sample selection for cancer classification with support vectors based t-statistic
作者： Piyushkumar A. Mundra , Jagath C. Rajapakse
DOI: 10.1016/J.NEUCOM.2010.02.025
关键词: t-statistic 、 Pattern recognition 、 Task (project management) 、 Filter (signal processing) 、 Artificial intelligence 、 Benchmark (computing) 、 Mathematics 、 Data mining 、 Feature selection 、 Sample selection 、 Gene 、 Data point
摘要: T-statistic is widely used for gene ranking in the analysis of microarray expressions. Such a filter based criterion generally computed using all training samples, which, however, may not be equally important classification task. In this paper, we decompose t-statistic into two parts, corresponding to relevant and irrelevant data points. The points are selected support vectors then compute feature selection. By simultaneously selecting genes, significantly better results achieved on synthetic as well several benchmark cancer datasets.
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