A comparative study of selected classification accuracy in user profiling

Cufoglu, Ayse and Lohi, Mahi and Madani, Kambiz (2008) A comparative study of selected classification accuracy in user profiling. In: ICMLA '08: The Seventh International Conference on Machine Learning and Applications; San Diego, CA, USA December 11-13, 2008. IEEE, pp. 787-791. ISBN 9780769534954

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Official URL: http://10.1109/ICMLA.2008.139

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.

Item Type: Book Section
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Miss Nina Watts
Date Deposited: 12 May 2009 09:59
Last Modified: 11 Aug 2010 14:35
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/6856

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