Bookcover of SUPPORT VECTOR MACHINE TEXT CLASSIFIER FOR ARABIC ARTICLES
Booktitle:

SUPPORT VECTOR MACHINE TEXT CLASSIFIER FOR ARABIC ARTICLES

USING ANT COLONY OPTIMIZATION-BASED FEATURE SUBSET SELECTION

VDM Verlag Dr. Müller (2010-07-01 )

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ISBN-13:

978-3-639-27141-6

ISBN-10:
3639271416
EAN:
9783639271416
Book language:
English
Blurb/Shorttext:
In this book, we have implemented a support vector machine (SVM) text classifier for Arabic articles. Experimental results show that the SVM classifier outperformed Naïve Bayesian (NB) and k-nearest neighbor (kNN) classifiers. We investigated the effectiveness of six state-of-the-art feature subset selection (FSS) methods, which are commonly used in text classification (TC) tasks, for our Arabic SVM text classification system. We implemented an Ant Colony Optimization Based-Feature Subset Selection (ACO Based-FSS) method for our Arabic SVM text classifier. The proposed FSS method adapted Chi-square statistic as heuristic information and the effectiveness of the SVM classifier as a guide to improving the selection of features for each category. Compared to the six state-of-the-art FSS methods, our ACO Based-FSS algorithm achieved better TC effectiveness. Evaluation used an in-house Arabic TC corpus that consists of 1445 documents independently classified into nine categories. The experimental results were presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F1 measures.
Publishing house:
VDM Verlag Dr. Müller
Website:
http://www.vdm-verlag.de
By (author) :
Abdelwadood Mesleh
Number of pages:
132
Published at:
2010-07-01
Stock:
Available
Category:
Informatics, IT
Price:
59.00 €
Keywords:
informatics, Computer, Information Retrieval

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