A self-adaptive Bayesian network classifier by means of genetic optimization
Hongshui Xu, Wei Huang, Jinsong Wang, Dan Wang · 2017
In the design of conventional Bayes network classifiers (e.g. Naive Bayes Classifier, Tree Augment Naive Bayes classifier), the network classifier structures are always fixed. Such network structures are very difficult to reflect the relationships among nodes (attributes). In this paper, we propose a self-adaptive Bayesian Network classifier based on genetic optimization. Genetic optimization is exploited here to realize the Self-adaptiveness, which means the network structure can be gradually optimized when constructing Bayesian network classifier. Experimental results show that the proposed method leads to a high classification accuracy than Naive Bayes classifier, Tree Augment Naive Bayes classifier, and KNN classifier on some benchmarks.