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Author Hongbing Wang ♦ Yanqi Shi ♦ Xuan Zhou ♦ Qianzhao Zhou ♦ Shizhi Shao ♦ Bouguettaya, A.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Web services ♦ Support vector machines ♦ Semantics ♦ Taxonomy ♦ Accuracy ♦ Classification algorithms ♦ Kernel ♦ Support Vector Machine ♦ Web Service Classification
Abstract Classification is a widely used mechanism for facilitating Web service discovery. Existing methods for automatic Web service classification only consider the case where the category set is small. When the category set is big, the conventional classification methods usually require a large sample collection, which is hardly available in real world settings. This paper presents a novel method to conduct service classification with a medium or big category set. It uses the descriptive information of categories in a large-scale taxonomy as sample data, so as to disengage from the dependence on sample service documents. A new feature selection method is introduced to enable efficient classification using this new type of sample data. We demonstrate the effectiveness of our classification method through extensive experiments.
ISBN 9781424488179
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-10-27
Publisher Place France
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 0.98 MB
Page Count 4
Starting Page 3
Ending Page 6

Source: IEEE Xplore Digital Library