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Outlier Modification and Gene Selection for Binary Cancer Classification using Gaussian Linear Bayes Classifier
Md. Hadiul Kabir, Md. Nurul Haque Mollah
Pages - 13 - 24     |    Revised - 31-08-2015     |    Published - 30-09-2015
Volume - 9   Issue - 2    |    Publication Date - September 2015  Table of Contents
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KEYWORDS
Gene Expression, Outlier Modification, Top DE Genes Selection, Binary Classification, Gaussian Bayes Classifier, Misclassification Error Rate (MER).
ABSTRACT
Gaussian linear Bayes classifier is one of the most popular approaches for classification. However, it is not so popular for cancer classification using gene expression data due to the inverse problem of its covariance matrix in presence of large number of gene variables with small number of cancer patients/samples in the training dataset. To overcome these problems, we propose few top differentially expressed (DE) genes from both upregulated and downregulated groups for binary cancer classification using the Gaussian linear Bayes classifier. Usually top DE genes are selected by ranking the p-values of t-test procedure. However, both t-test statistic and Gaussian linear Bayes classifier are sensitive to outliers. Therefore, we also propose outlier modification for gene expression dataset before applying to the proposed methods, since gene expression datasets are often contaminated by outliers due to several steps involves in the data generating process from hybridization to image analysis. The performance of the proposed method is investigated using both simulated and real gene expression datasets. It is observed that the proposed method improves the performance with outlier modifications for binary cancer classification.
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Mr. Md. Hadiul Kabir
Department of Statistics, University of Rajshahi, Bangladesh - Bangladesh
Professor Md. Nurul Haque Mollah
University of Rajshahi, Bangladesh - Bangladesh
mollah.stat.bio@ru.ac.bd