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BioMed Research International
Volume 2017, Article ID 3020627, 17 pages
https://doi.org/10.1155/2017/3020627
Research Article

Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis

1Lab of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh
2Department of Statistics, Begum Rokeya University, Rangpur, Rangpur 5400, Bangladesh

Correspondence should be addressed to Md. Shakil Ahmed; moc.liamg@urtats.likahs

Received 18 March 2017; Revised 10 June 2017; Accepted 14 June 2017; Published 7 August 2017

Academic Editor: Federico Ambrogi

Copyright © 2017 Md. Shakil Ahmed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The naïve Bayes classifier (NBC) is one of the most popular classifiers for class prediction or pattern recognition from microarray gene expression data (MGED). However, it is very much sensitive to outliers with the classical estimates of the location and scale parameters. It is one of the most important drawbacks for gene expression data analysis by the classical NBC. The gene expression dataset is often contaminated by outliers due to several steps involved in the data generating process from hybridization of DNA samples to image analysis. Therefore, in this paper, an attempt is made to robustify the Gaussian NBC by the minimum -divergence method. The role of minimum -divergence method in this article is to produce the robust estimators for the location and scale parameters based on the training dataset and outlier detection and modification in test dataset. The performance of the proposed method depends on the tuning parameter . It reduces to the traditional naïve Bayes classifier when . We investigated the performance of the proposed beta naïve Bayes classifier (-NBC) in a comparison with some popular existing classifiers (NBC, KNN, SVM, and AdaBoost) using both simulated and real gene expression datasets. We observed that the proposed method improved the performance over the others in presence of outliers. Otherwise, it keeps almost equal performance.