title: Fuzzy Naive Bayesian for constructing regulated network with weights.
abstract: In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. Then, we can use regulated relations and weights to construct a regulated network. Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. The experiment results show that the FNB has a higher classification rate than Naive Bayesian and TAN.
authors.affiliation: IT College, Gachon University, Seongnam, South Korea.
authors.forename: Xi Y
authors.initials: XY
authors.lastname: Zhou
authors.affiliation: Department of Educational Technology, Teachers College, Qingdao University, QingDao, China.
authors.forename: Xue W
authors.initials: XW
authors.lastname: Tian
authors.affiliation: IT College, Gachon University, Seongnam, South Korea.
authors.forename: Joon S
authors.initials: JS
authors.lastname: Lim
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Fuzzy Naive Bayesian; Naive Bayesian; Tree Augmented Naive Bayesian; fuzzy neural network; weights