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Multi-Label Genre Classification of Web Pages Using an Adaptive Centroid-Based Classifier

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  • Chaker Jebari

    (IT Department, College of Applied Sciences, IBRI, BOX 516, Sultanate of Oman)

Abstract

This paper proposes an adaptive centroid-based classifier (ACC) for multi-label classification of web pages. Using a set of multi-genre training dataset, ACC constructs a centroid for each genre. To deal with the rapid evolution of web genres, ACC implements an adaptive classification method where web pages are classified one by one. For each web page, ACC calculated its similarity with all genre centroids. Based on this similarity, ACC either adjusts the genre centroid by including the new web page or discards it. A web page is a complex object that contains different sections belonging to different genres. To handle this complexity, ACC implements a multi-label classification where a web page can be assigned to multiple genres at the same time. To improve the performance of genre classification, we propose to aggregate the classifications produced using character n-grams extracted from URL, title, headings and anchors. Experiments conducted using a known multi-label dataset show that ACC outperforms many other multi-label classifiers and has the lowest computational complexity.

Suggested Citation

  • Chaker Jebari, 2016. "Multi-Label Genre Classification of Web Pages Using an Adaptive Centroid-Based Classifier," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-21, March.
    • Handle: RePEc:wsi:jikmxx:v:15:y:2016:i:01:n:s0219649216500088
    DOI: 10.1142/S0219649216500088
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    References listed on IDEAS

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    1. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
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    Cited by:

    1. Hanan Al-Mofareji & Mahmoud Kamel & Mohamed Y. Dahab, 2017. "WeDoCWT: A New Method for Web Document Clustering Using Discrete Wavelet Transforms," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-19, March.
    2. Ruchika Malhotra & Anjali Sharma, 2017. "Quantitative evaluation of web metrics for automatic genre classification of web pages," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1567-1579, November.

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