Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study

Cufoglu, Ayse and Lohi, Mahi and Madani, Kambiz (2008) Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study. In: IEEE International Conference on Computer Engineering and Systems (ICCES'08), Cairo, Egypt, 25 - 27 Nov 2008. IEEE, pp. 210-215. ISBN 9781424421152

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Official URL: http://dx.doi.org/10.1109/ICCES.2008.4772998

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. The obtained simulation results have been evaluated against the existing works of support vector machines (SVMs), decision trees (DTs) and neural networks (NNs).

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 09:53
Last Modified: 11 Aug 2010 14:35
URI: http://westminsterresearch.wmin.ac.uk/id/eprint/6855

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