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Enhancing Web Page Classification Models

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Book cover Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

Abstract

Web page classification has a crucial role in web mining. The massive amount of data available on the web makes it so important to build web page prediction models. We aim to build classification models that classify new instances depending on existing labeled web documents. This paper investigates the effect of the two powerful ensemble methods called stacked generalization-also known as stacking- and random forest in web page classification context. In this paper, we suggest to enhance the predictive power of the web page classification models by stacking ensemble method. Random forest, stacking with multi-response model trees and four different base learners (Naïve Bayes, J4.8, IBK and FURIA) are used. Datasets are obtained from DMOZ (Open Directory Project). This paper provides an empirical study on the existing supervised classifiers and ensemble learning methods in web page classification context. It introduces that constructing ensembles of heterogeneous classifiers with stacking has higher predictive power than the individual classifiers, boosting and random forest for web page classification.

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Author information

Authors and Affiliations

  1. College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport, PO Box 1029, Alexandria, Egypt

    Fayrouz Elsalmy & Walid AbdelMoez

  2. Ain Shams University, Cairo, Egypt

    Rasha Ismail

Authors
  1. Fayrouz Elsalmy
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  2. Rasha Ismail
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  3. Walid AbdelMoez
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Corresponding author

Correspondence to Fayrouz Elsalmy .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Elsalmy, F., Ismail, R., AbdelMoez, W. (2017). Enhancing Web Page Classification Models. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_71

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_71

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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