Improving naive Bayes classifiers with high-dimensional non-Gaussian data

1191 

Contributed Abstract 

Poster 

Mijin Jeong (1), Gyuhyeong Goh (2), Dipak Dey (3)

(1) Kyungpook National University, N/A, (2) Department of Statistics, Kyungpook National University, N/A, (3) University of Connecticut, N/A

Gyuhyeong Goh  
Department of Statistics, Kyungpook National University

Dipak Dey  
University of Connecticut

Mijin Jeong  
Kyungpook National University

Mijin Jeong  
Kyungpook National University

The naive Bayes classifier, which assumes the conditional independence of predictors, improves classification efficiency and has a great advantage in handling high-dimensional data as well as imbalanced data. However, the success of the naive Bayes classifier hinges on the normality assumption for each continuous predictor and its performance decreases considerably as many irrelevant predictor are included.
In this paper, we develop a way of improving the performance of naive Bayes classifiers when we deal with high-dimensional non-Gaussian data. To remove irrelevant predictors, we develop an efficient variable selection procedure in the context of naive Bayes classification using the notion of Bayesian Information Criteria (BIC). In addition, we adapt the naive Bayes classifier for use with non-Gaussian data via power transformation. We conduct a comparative simulation study to demonstrate the superiority of our proposed classifier over existing classification methods. We also apply our proposed classifier to real data and confirm its effectiveness.

Bayes classifier|Generative classifier|High-dimensional variable selection|Power transformation| |

Section on Statistical Learning and Data Science

Machine Learning