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Removing Non-informative Features by Robust Feature Wrapping Method for Microarray Gene Expression Data  

Lee, Jae-Sung (중앙대학교 컴퓨터공학과)
Kim, Dae-Won (중앙대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.35, no.8, 2008 , pp. 463-478 More about this Journal
Abstract
Due to the high dimensional problem, typically machine learning algorithms have relied on feature selection techniques in order to perform effective classification in microarray gene expression datasets. However, the large number of features compared to the number of samples makes the task of feature selection computationally inprohibitive and prone to errors. One of traditional feature selection approach was feature filtering; measuring one gene per one step. Then feature filtering was an univariate approach that cannot validate multivariate correlations. In this paper, we proposed a function for measuring both class separability and correlations. With this approach, we solved the problem related to feature filtering approach.
Keywords
Bioinformatics; HCA; Genetic algorithm; Feature selection; Correlation coefficient;
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