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2008 | OriginalPaper | Chapter

Support Vector Machines based Arabic Language Text Classification System: Feature Selection Comparative Study

Author : Abdelwadood. Moh’d. Mesleh

Published in: Advances in Computer and Information Sciences and Engineering

Publisher: Springer Netherlands

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feature selection (FS) is essential for effective and more accurate text classification (TC) systems. This paper investigates the effectiveness of five commonly used FS methods for our Arabic language TC System. Evaluation used an in-house collected Arabic TC corpus. The experimental results are presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F1 measure.

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Metadata
Title
Support Vector Machines based Arabic Language Text Classification System: Feature Selection Comparative Study
Author
Abdelwadood. Moh’d. Mesleh
Copyright Year
2008
Publisher
Springer Netherlands
Book
Advances in Computer and Information Sciences and Engineering

Print ISBN: 978-1-4020-8740-0

Electronic ISBN: 978-1-4020-8741-7

Copyright Year: 2008

DOI
https://doi.org/10.1007/978-1-4020-8741-7_3

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