Irrelevant gene elimination for Partial Least Squares based Dimension Reduction by using feature probes
by Xue-Qiang Zeng, Guo-Zheng Li, Geng-Feng Wu, Jack Y. Yang, Mary Qu Yang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 3, No. 1, 2009

Abstract: It is hard to analyse gene expression data which has only a few observations but with thousands of measured genes. Partial Least Squares based Dimension Reduction (PLSDR) is superior for handling such high dimensional problems, but irrelevant features will introduce errors into the dimension reduction process. Here, feature selection is applied to filter the data and an algorithm named PLSDR^g is described by integrating PLSDR with gene elimination, which is performed by the indication of t-statistic scores on standardised probes. Experimental results on six microarray data sets show that PLSDR^g is effective and reliable to improve generalisation performance of classifiers.

Online publication date: Tue, 17-Mar-2009

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