Please use this identifier to cite or link to this item:
Title: Customizable instance-driven webpage filtering using graph-based semi-supervised active learning
Authors: Ding, X.
Guo, W.
Bao, B. 
Zhu, M.
Wang, Z.
Keywords: Active learning
Customizable instance
Link information
Semi-supervised learning
Web page classification
Issue Date: Dec-2011
Citation: Ding, X.,Guo, W.,Bao, B.,Zhu, M.,Wang, Z. (2011-12). Customizable instance-driven webpage filtering using graph-based semi-supervised active learning. Journal of Information and Computational Science 8 (15) : 3659-3666. ScholarBank@NUS Repository.
Abstract: The World Wide Web has been growing rapidly in recent years, along with increasing needs for content-based webpage filtering. Most of the existing filtering systems, however, cannot easily satisfy the personalized filtering demands from different users at the same time. To address this issue, this paper presents a customizable instance-driven webpage filter strategy, which utilizes graph-based semi-supervised active learning. In the proposed strategy, a semi-supervised active learning approach is applied for obtaining a precise description of the webpage class based on the small-sized user instance set provided by user himself. Subsequently, a Bayes classifier is created over the enlarged training set. By this way, different webpage filters are produced by using users' own demands, so that the users are able to focus on their interested classes. Experimental results show the promise stability and high performance of our proposed method. © 2009 by Binary Information Press.
Source Title: Journal of Information and Computational Science
ISSN: 15487741
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Sep 9, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.