A Novel Naive Bayes Classification Algorithm Based on Particle Swarm Optimization

ABSTRACT

Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent assumption of its attribute limits the application of the actual data. This paper presents an approach called particle swarm optimization-naive Bayes (PSO-NB) which takes advantage of combination particle swarm optimization with naive Bayes for attribute selection to improve naive Bayes classifier. This method applies PSO firstly to search out an optimal subset of attributes reduction in the original attribute space, and then constructs a naive Bayes classifier on the gotten subset of the attributes reduction. Nineteen experimental results on UCI datasets distinctly show that compared with Cfs-BestFirst algorithm, NB algorithm, Decision Tree(C4.5) algorithm, K-neighbor(KNN) algorithm, the proposed algorithm has higher classification accuracy.

Keywords:

Naive Bayes, particle swarm optimization algorithm, feature selection, attribute subset, classification accuracy.