The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 53-106
Full Name: Ong Hong Choon
Position: Senior Lecturer
Age: ON
Sex: Male
Address: School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
Country: MALAYSIA
Tel: 6016-4981802
Tel prefix:
Fax: 604-6570910
E-mail address: hcong@cs.usm.my
Other E-mails: onghongchoon@gmail.com
Title of the Paper: Structural Learning for Improving Bayesian Network Classification
Authors as they appear in the Paper: Ong Hong Choon
Email addresses of all the authors: hcong@cs.usm.com
Number of paper pages: 10
Abstract: Bayesian networks are probabilistic graphical models which can handle efficient uncertainty reasoning with hundreds of variables. They were not considered classification tools until the discovery of Naïve Bayes as classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of structural learning is proposed to be applied where there are relationships between the features when applying the Naïve Bayes. The improvement in classification using structural learning is shown if there exist relationships between the features or when they are not independent. The result is shown using six data sets.
Keywords: Structural Learning, Bayesian Network, Classification, Naïve Bayes, Data Mining.
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