Analysis and Optimization of the Naïve Bayes Classifiers Using Cancer Datasets

Abstract

Naïve Bayes Classifier is an important classifier of the Bayesian network. In this paper, the relative strengths and weaknesses of different classification methods are discussed. Various methods of data analysis and processing are also studied. Naïve Bayes classification algorithm gives unsatisfactory result on dependent feature values. The accuracy of the Bayesian network depends on the dataset independency which is not the case for the real world dataset items. A method to optimize the Naïve Bayes classification algorithm has been proposed in this paper. Several cancer datasets were selected and applied to the proposed classifier which is compared with other classifiers.

Index Terms: Data Mining, Classification, Naïve Bayes Classification, Data Clustering

Cite this Article

S. M. Sadrul Islam Asif, Rizoan Toufiq, Md. Rabiul Islam. Analysis and Optimization of The Naïve Bayes Classifiers Using Cancer Datasets. Research and Reviews: Journal of Computational Biology (RRJoCB). 2015; 4(2): 8–12p.