A Comparative Study of Selected Classification Accuracy in User Profiling

Author

Cufoglu, Ayse ; Lohi, Mahi ; Madani, Kambiz

Author_Institution

Dept. of Electron., Commun. & Software Eng. Univ. of Westminster London, London

fYear

2008

fDate

11-13 Dec. 2008

Firstpage

787

Lastpage

791

Abstract

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian Networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate.

Keywords

Bayes methods; belief networks; learning (artificial intelligence); pattern classification; Bayesian network; Bayesian rule; classification algorithm; instance-based learner; lazy learning; naive Bayesian algorithm; user profile; Application software; Bayesian methods; Classification algorithms; Decision trees; Machine learning; Machine learning algorithms; Niobium; Robust stability; Support vector machine classification; Support vector machines; Bayesian networks; Classification Accuracy; Instance Based Learner; Lazy learning of Bayesian Rules; Naive Bayesian; User Profile;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on

Conference_Location

San Diego, CA

Print_ISBN

978-0-7695-3495-4

Type

conf

DOI

10.1109/ICMLA.2008.139

Filename

4725067