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A Comparison Study of Bayesian Classifiers on Web Pages Classification

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Abstract

With the development of internet, web mining has become a hotspot of data mining. The first step of web mining is to classify web pages into interesting classes, so the classification is one of the essential techniques for web mining. In this paper, we study the capabilities of bayesian classifiers for web pages categorization, after that we report our work on the comparison of binary-classification and multi-classification. Results on benchmark dataset show that bayesian classifiers perform satisfying, especially for Hidden Naive Bayes (HNB) which is more competitive than other methods. Furthermore, the performances of binary-classification are better than those of multi-classification under the evaluation metrics of accuracy and F-measure.

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References

  1. Sahami, M., Dumais, S., Heckerman, D. and Horvitz, E., “A bayesian approach to filtering junk e-mail,” in Learning for Text Categorization, Madison, Wisconsin, 1998.

  2. Metsis, V., Androutsopoulos, I., Paliouras, G., “Spam Filtering with Naive Bayes - Which Naive Bayes,” CEAS 2006 Third Conference on Email and Anti-Spam, Mountain View, California, USA., Jul. 2006.

  3. Zhang, H., Jiang, L., Su, J., “Hidden Naive Bayes,” in Proc. of the Twentieth National Conference on Artificial Intelligence, AAAI Press, pp. 919-924, 2005.

  4. Webb, G. I., Boughton, J. R., Wang, Z., “Not So Naive Bayes: Aggregating One-Dependence Estimators,” Machine Learning , 58, 5-24, 2005.

  5. Mase, H., “Experiments on AutomaticWeb Page Categorization for IR System,” Technical Report, Stanford University, Calif., 1998.

  6. Rennie, J. D. M., Shih, L., Teevan, J., Karger, D. R., “Tackling the Poor Assumptions of Naive Bayes Text Classifiers,” in Proc. of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC., 2003.

  7. Industry Sector Dataset, http://www.cs.cmu.edu/TextLearning/datasets.html, 2005.

  8. Kira, K., Rendell, L. A., “The Feature Selection Problem: Traditional Methods and New Algorithm,” in Proc. of AAAI 92, 1992 .

  9. Kira, K., Rendell, L. A., “A Practical Approach to Feature Selection,” in Proc. of International Conference, pp. 249-256, Morgan Kaufmann, 1992.

  10. Buddhinath, G., Derry, D., “A Simple Enhancement to One Rule Classification,” Technique Report at http://goanna.cs.rmit.edu.au/gjay-atil/OtherLinks/Extra.ph, 2006.

  11. Stemming, http://www.comp.lancs.ac.uk/computing/research/stemming/general/., 2006.

  12. Yu, L., Liu, H., “Feature selection for high-dimensional data: a fast correlationbased filter solution,” in Proc. of ICML 2003, 2003.

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Authors and Affiliations

  1. Department of Computer Science, Beijing Normal University, Beijing, China

    Rongfang Bie, Zengmei Fu, Qiurui Sun & Chuanliang Chen

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  1. Rongfang Bie
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  2. Zengmei Fu
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  3. Qiurui Sun
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  4. Chuanliang Chen
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Correspondence to Rongfang Bie.

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Bie, R., Fu, Z., Sun, Q. et al. A Comparison Study of Bayesian Classifiers on Web Pages Classification. New Gener. Comput. 28, 161–168 (2010). https://doi.org/10.1007/s00354-008-0083-3

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  • DOI: https://doi.org/10.1007/s00354-008-0083-3

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