Gene feature extraction using T-test statistics and kernel partial least squares

Authors:
Shutao Li;Chen Liao;James T. Kwok
Affiliations:
College of Electrical and Information Engineering, Hunan University, Changsha, China;College of Electrical and Information Engineering, Hunan University, Changsha, China;Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

In this paper, we propose a gene extraction method by using two standard feature extraction methods, namely the T-test method and kernel partial least squares (KPLS), in tandem. First, a preprocessing step based on the T-test method is used to filter irrelevant and noisy genes. KPLS is then used to extract features with high information content. Finally, the extracted features are fed into a classifier. Experiments are performed on three benchmark datasets: breast cancer, ALL/AML leukemia and colon cancer. While using either the T-test method or KPLS does not yield satisfactory results, experimental results demonstrate that using these two together can significantly boost classification accuracy, and this simple combination can obtain state-of-the-art performance on all three datasets.