Chapter title | A comparative study of selected classification accuracy in user profiling |
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Authors | Cufoglu, A., Lohi, M. and Madani, K. |
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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. |
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Book title | ICMLA '08: The Seventh International Conference on Machine Learning and Applications; San Diego, CA, USA December 11-13, 2008 |
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Page range | 787-791 |
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Year | 2008 |
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Publisher | IEEE |
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Publication dates |
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Published | 2008 |
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ISBN | 9780769534954 |
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Digital Object Identifier (DOI) | https://doi.org/10.1109/ICMLA.2008.139 |
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File | |
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Web address (URL) | http://10.1109/ICMLA.2008.139 |
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