Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis.
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 imp...
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 18 |
|---|---|
| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/7/2017
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124508788&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124508788 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/7/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 124508788 124508788 124508788 10.1155/2017/3020627 124508788 ppf: 1 ppct: 17 formats: fmt: @attributes: type: P tig: atl: Robustification of Naïve Bayes Classifier and Its Application for Microarray Gene Expression Data Analysis. aug: au: Ahmed, Md. Shakil Shahjaman, Md. Rana, Md. Masud Haque Mollah, Md. Nurul affil: Lab of Bioinformatics, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh sug: subj: Microarray Analysis Gene Expression Human Descriptive Statistics Data Analysis Software Computer Simulation Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|