Learning an Optimal Naive Bayes Classifier

Authors:
Miriam Martinez-Arroyo;L. Enrique Sucar
Affiliations:
Instituto Tecnologico de Acapulco, Acapulco, Guerrero, Mexico;INAOE Tonantzintla, Puebla, Mexico
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
Cited 3

Computerized Lung Nodule Detection Using 3D Feature Extraction and Learning Based Algorithms

Journal of Medical Systems
Optimizations of the naïve-Bayes classifier for the prognosis of B-Chronic Lymphocytic Leukemia incorporating flow cytometry data

Computer Methods and Programs in Biomedicine
Classification of Juvenile Myoclonic Epilepsy Data Acquired Through Scanning Electromyography with Machine Learning Algorithms

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The naive Bayes classifier is an efficient classification model that is easy to learn and has a high accuracy in many domains. However, it has two main drawbacks: (i) its classification accuracy decreases when the attributes are not independent, and (ii) it can not deal with nonparametric continuous attributes. In this work we propose a method that deals with both problems, and learns an optimal naive Bayes classifier. The method includes two phases, discretization and structural improvement, which are repeated alternately until the classification accuracy can not be improved. Discretization is based on the minimum description length principle. To deal with dependent and irrelevant attributes, we apply a structural improvement method that eliminates and/or joins attributes, based on mutual and conditional information measures. The method has been tested in two different domains with good results.