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Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis
Joint Authors
Ahmed, Md. Shakil
Shahjaman, Md.
Rana, Md. Masud
Mollah, Md. Nurul Haque
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-07
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
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 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 β.
It reduces to the traditional naïve Bayes classifier when β→0.
We investigated the performance of the proposed beta naïve Bayes classifier (β-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.
American Psychological Association (APA)
Ahmed, Md. Shakil& Shahjaman, Md.& Rana, Md. Masud& Mollah, Md. Nurul Haque. 2017. Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis. BioMed Research International،Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1135643
Modern Language Association (MLA)
Ahmed, Md. Shakil…[et al.]. Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis. BioMed Research International No. 2017 (2017), pp.1-17.
https://search.emarefa.net/detail/BIM-1135643
American Medical Association (AMA)
Ahmed, Md. Shakil& Shahjaman, Md.& Rana, Md. Masud& Mollah, Md. Nurul Haque. Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1135643
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135643