|Chapter title||A comparative study of selected classification accuracy in user profiling|
|Authors||Cufoglu, A., Lohi, M. and Madani, K.|
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.
|Book title||ICMLA '08: The Seventh International Conference on Machine Learning and Applications; San Diego, CA, USA December 11-13, 2008|
|Digital Object Identifier (DOI)||https://doi.org/10.1109/ICMLA.2008.139|
|Web address (URL)||http://10.1109/ICMLA.2008.139|