NDLI: Random forest classifier for multi-category classification of web pages

Content Provider	IEEE Xplore Digital Library
Author	Win Thanda Aung Khin Hay Mar Saw Hla
Copyright Year	2009
Description	Author affiliation: University of Computer Studies, Yangon, Myanmar (Win Thanda Aung; Khin Hay Mar Saw Hla)
Abstract	Web page classification is the automated assigning of predefined subject category to the document. Automatic web page classification is one of the most essential techniques for Web Mining given that the web is a huge repository of various information including images, videos etc. And there is a need for categorization web pages to satisfy user needs. The classification of web pages into each category exclusively relies on man power which cost much time and effort. To alleviate this manually classification problem, more researchers focus on the issue of web pages classification technology. In this paper, we proposed Random Forest Classifier (RF) based on random forest method for multi-category web page classification. The proposed RF classifier can classify web pages efficiently according to their corresponding class without using other feature selection methods. We compared the accuracy of the proposed approach to decision tree classifier using in the same Yahoo web pages. The experiments have shown that the proposed approach is suitable for the multi-category web page classification.
Starting Page	372
Ending Page	376
File Size	145927
Page Count	5
File Format	PDF
ISBN	9781424453382
DOI	10.1109/APSCC.2009.5394100
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-07
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radio frequency Support vector machines Machine learning algorithms random forest classifier Neural networks Web pages Support vector machine classification Training data multi-category web page 11classification Decision trees Classification tree analysis Nearest neighbor searches
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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