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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
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:6856
Deposited On:12 May 2009 10:59
Last Modified:11 Aug 2010 15:35

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