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

Cufoglu, A., Lohi, M. and Madani, K. 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: Fahmy, H.M.A., Wahba, A.M., El-Kharashi, M.W., El-Din, A.M.B., Sobh, M.A. and Tahar, M. (ed.) IEEE International Conference on Computer Engineering and Systems (ICCES'08), Cairo, Egypt, 25 - 27 Nov 2008 IEEE . pp. 210-215

Chapter titleClassification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study
AuthorsCufoglu, A., Lohi, M. and Madani, K.
EditorsFahmy, H.M.A., Wahba, A.M., El-Kharashi, M.W., El-Din, A.M.B., Sobh, M.A. and Tahar, M.
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).

Book titleIEEE International Conference on Computer Engineering and Systems (ICCES'08), Cairo, Egypt, 25 - 27 Nov 2008
Page range210-215
Year2008
PublisherIEEE
Publication dates
Published2008
ISBN9781424421152
Digital Object Identifier (DOI)https://doi.org/10.1109/ICCES.2008.4772998
File

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