Feature Selection for Classification of Gene Expression Data

نویسندگان

  • Zuraini Ali Shah
  • Puteh Saad
  • Razib M. Othman
چکیده

This paper presents a gene selection method for classification of gene expression data. First, a fFeature selection techniques based on t-test statistic is used applied in order to select the n topranked significant genes. Then, a combination of Genetic Algorithm (GA) and /Support Vector Machines (SVM) is used to further select significant genes to the resulting set of genes obtained from t-test selection.improve the selection of t-test. SVM classifier is utilized implemented to evaluate performance of the feature selection approaches. the final gene. Results of comparative studies are provided, demonstrating that effective feature selection is essential to the development of classifiers intended for to be used in for high-dimensionality data problems. The result of SVM classifier with all genes compared with ttest/SVM and t-test/GA/SVM procedure are reported. This research also shows that feature selection helps increase computational efficiency while improving classification accuracy.

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تاریخ انتشار 2009