Title :
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
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;
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
DOI :
10.1109/ICMLA.2008.139
