A Comparative Study of Selected Classifiers with Classification Accuracy in User Profiling

In recent years the use of personalized service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. In literature a number of classification algorithms have been used to classify user related information to create accurate user profiles. Nevertheless, there is lack of comparison of these algorithms with classification accuracy of the user profile information. In our previous work [1], we compared four different classification algorithms which are; Naive Bayesian (NB), Instance-Based Learner (IB1), Bayesian networks (BN) and Lazy Learning of Bayesian Rules (LBR) classifiers. According to our results NB and IB1 classifiers outperformed the BN and LBR classifiers with respect to classification accuracy. In this study we compare the performance of NB, IB1, Classification and Regression Tree (SimpleCART), Naive Bayesian Tree (NBTree), Iterative Dichotomister Tree (Id3), J48 -a version of C4.5- and Sequential Minimal Optimization (SMO) algorithms with large user profile data. This study is aimed to find the best classification algorithm for user profiling process. Our simulation results show that, in general, the NBTree has the highest classification accuracy performance with the lowest error rate. On the other hand, we also found that the NBTree has one of the highest time requirements to build the classification model. Therefore, NBTree classification algorithm should be favoured over SMO, NB, IB1, J48, SimpleCART and Id3 classifiers in the personalization applications especially when the classification accuracy performance is important.