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

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 β-divergence method. The role of minimum β-divergence method in this article is to produce the robust estimators for the location and scale paramet

SUBMITTER: Ahmed MS 

PROVIDER: S-EPMC5564130 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature