[Aha, D. W., Kibler, D., and Albert, M. K. 1991. Instance-based learning algorithms. Machine Learning 6, 1 (Jan.), 37-66.]Search in Google Scholar
[Baykan, E., Henzinger, M., Marian, L., and Weber, I. 2011. A comprehensive study of features and algorithms for url-based topic classification. ACM Transactions on Web 5, 3 (July), 15:1-15:29.]Search in Google Scholar
[Berry, M. W. and Kogan, J., Eds. 2010. Text Mining: Applications and Theory. Wiley, Chichester, UK.10.1002/9780470689646]Search in Google Scholar
[Breiman, L. 2001. Random forests. Machine Learning 45, 1 (Oct.), 5-32.]Search in Google Scholar
[Chang, C.-C. and Lin, C.-J. 2001. Libsvm - a library for support vector machines. The Weka classifier works with version 2.82 of LIBSVM.]Search in Google Scholar
[Devi, M. I., Rajaram, R., and Selvakuberan, K. 2007. Machine learning techniques for automated web page classification using url features. In ICCIMA ’07: Proceedings of theInternational Conference on Computational Intelligence and Multimedia Applications. Vol. 2. IEEE Computer Society, Washington, DC, USA, 116-120.]Search in Google Scholar
[Esfandiari, B. and Nock, R. 2005. Adaptive filtering of advertisements on web pages. In WWW ’05: Special interest tracks and posters of the 14th international conference on WorldWide Web. ACM, New York, NY, USA, 916-917.]Search in Google Scholar
[Fayyad, U. and Irani, K. 1993. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. IJCAI ’93: Proceedings of the 13th International Joint Conferenceon Artificial Intelligence, 1022-1027.]Search in Google Scholar
[Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research 3, 1289-1305.]Search in Google Scholar
[Freund, Y. and Schapire, R. E. 1995. A decision-theoretic generalization of on-line learning and an application to boosting. In EuroCOLT ’95: Proceedings of the 2nd European Conferenceon Computational Learning Theory. Springer-Verlag, London, UK, 23-37.]Search in Google Scholar
[Freund, Y. and Schapire, R. E. 1996. Experiments with a New Boosting Algorithm. In ICML’96: Proceedings of the 13th International Conference on Machine Learning. 148-156.]Search in Google Scholar
[George, J. and Pat, L. 1995. Estimating continuous distributions in bayesian classifiers. In In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 338-345.]Search in Google Scholar
[Kan, M. Y. 2004. Web page classification without the web page. In WWW Alt. ’04: Proceedingsof the 13th international World Wide Web conference on Alternate track papers & posters. ACM, New York, NY, USA, 262-263.]Search in Google Scholar
[Kan, M.-Y. and Thi, H. O. N. 2005. Fast webpage classification using url features. In CIKM’05: Proceedings of the 14th ACM international conference on Information and knowledgemanagement. ACM, New York, NY, USA, 325-326.]Search in Google Scholar
[Krammer, V. 2008. An effective defense against intrusive web advertising. In PST ’08: Proceedingsof the 2008 Sixth Annual Conference on Privacy, Security and Trust. IEEE Computer Society, Washington, DC, USA, 3-14.]Search in Google Scholar
[Moran, S., He, Y., and Liu, K. 2009. Choosing the best bayesian classifier: an empirical study. IAENG International Journal of Computer Science 36, 4, 322-331.]Search in Google Scholar
[Quinlan, J. R. 1992. C4.5: Programs for Machine Learning (Morgan Kaufmann Series inMachine Learning), 1 ed. Morgan Kaufmann.]Search in Google Scholar
[Shih, L. K. and Karger, D. R. 2004. Using urls and table layout for web classification tasks. In WWW ’04: Proceedings of the 13th international conference on World Wide Web. ACM, New York, NY, USA, 193-202.]Search in Google Scholar
[Singh, A. K. and Potdar, V. 2009. Blocking online advertising - a state of the art. In ICIT’09: Proceedings of the 2009 IEEE International Conference on Industrial Technology. IEEE Computer Society, Washington, DC, USA, 1-10.]Search in Google Scholar
