research-article

Floatcascade learning for fast imbalanced web mining

Authors:

Zhong SuAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 71 - 80

https://doi.org/10.1145/1367497.1367508

Published: 21 April 2008 Publication History

Abstract

This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the "Matthew Effect". As web IC applications usually need to provide online service for user and deal with large volume of data, classification speed emerges as an important issue to be addressed. In face detection, Asymmetric Cascade is used to speed up imbalanced classification by building a cascade structure of simple classifiers, but it often causes a loss of classification accuracy due to the iterative feature addition in its learning procedure. In this paper, we adopt the idea of cascade classifier in imbalanced web mining for fast classification and propose a novel asymmetric cascade learning method called FloatCascade to improve the accuracy. To the end, FloatCascade selects fewer yet more effective features at each stage of the cascade classifier. In addition, a decision-tree scheme is adopted to enhance feature diversity and discrimination capability for FloatCascade learning. We evaluate FloatCascade through two typical IC applications in web mining: web page categorization and citation matching. Experimental results demonstrate the effectiveness and efficiency of FloatCascade comparing to the state-of-the-art IC methods like Asymmetric Cascade, Asymmetric AdaBoost and Weighted SVM.

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Cited By

Zhang XGuo ZGuo HZhu HSu ZHuang JKoudas NJones GWu XCollins-Thompson KAn A(2010)CasJoinProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871714(1725-1728)Online publication date: 26-Oct-2010
https://dl.acm.org/doi/10.1145/1871437.1871714
Yang XSun NZhang YKong D(2008)General Framework for Text Classification Based on Domain OntologyProceedings of the 2008 Third International Workshop on Semantic Media Adaptation and Personalization10.1109/SMAP.2008.17(147-152)Online publication date: 15-Dec-2008
https://dl.acm.org/doi/10.1109/SMAP.2008.17

Index Terms

Floatcascade learning for fast imbalanced web mining
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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New York, NY, United States

Publication History

Published: 21 April 2008

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WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

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Cited By

Zhang XGuo ZGuo HZhu HSu ZHuang JKoudas NJones GWu XCollins-Thompson KAn A(2010)CasJoinProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871714(1725-1728)Online publication date: 26-Oct-2010
https://dl.acm.org/doi/10.1145/1871437.1871714
Yang XSun NZhang YKong D(2008)General Framework for Text Classification Based on Domain OntologyProceedings of the 2008 Third International Workshop on Semantic Media Adaptation and Personalization10.1109/SMAP.2008.17(147-152)Online publication date: 15-Dec-2008
https://dl.acm.org/doi/10.1109/SMAP.2008.17

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