Comparison and Research of Bayesian Classification Algorithm in Relational Learning
Chunying Zhang · Journal of Hebei Polytechnic University · 2011
Data classification is one of the main content of data mining.Through analyzing training data samples,it is resulted in the accurate description on the classification.Bayesian classification is an effective simple classification algorithm in the field of data mining.In the relational learning,there are many kinds of Bayesian classification algorithms.It would be of considerable help to improve classification efficiency that summarize,compare these algorithms and point out its advantages and disadvantages.In this paper,some algorithms have made a detailed comparison and summary.In single relational learning,it is focus on several Bayesian classification algorithms based on Rough set and weighted Bayesian classification algorithms and analysis the models,methods to determine weights,advantages,disadvantages and the direction of further work.In multi-relational learning,the main comparison is several kinds of Bayesian classification algorithms based on Semantic relationship graph and focuses on the MI-MRNBC model.Finally,it is the summary and prospect of this article.The direction of further work is to study multi-relational Bayesian classification algorithm based on Rough set.