Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique

Authors

  • Oyebayo Ridwan Olaniran Universiti Tun Hussein Onn Malaysia
  • Mohd Asrul Affendi Abdullah Department of Mathematics and Statistics, Faculty of Applied Science and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh, Educational Hub, 84600 Pagoh, Johor, Malaysia

DOI:

https://doi.org/10.17713/ajs.v48i2.806

Abstract

In this paper, the one-way ANOVA model and its application in Bayesian multi-class variable selection is considered. A full Bayesian bootstrap prior ANOVA test function is developed within the framework of parametric empirical Bayes. The test function developed was later used for variable screening in multiclass classification scenario. Performance comparison between the proposed method and existing classical ANOVA method was achieved using simulated and real life gene expression datasets. Analysis results revealed lower false positive rate and higher sensitivity for the proposed method.

Published

2019-01-26

How to Cite

Olaniran, O. R., & Abdullah, M. A. A. (2019). Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique. Austrian Journal of Statistics, 48(2), 63-72. https://doi.org/10.17713/ajs.v48i2.806

Issue

Vol. 48 No. 2 (2019): Regular Issue

Section

Articles

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