NDLI: A Hybrid Text Classification Model based on Rough Sets and Genetic Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyue Wang Zhen Hua Rujiang Bai
Copyright Year	2008
Description	Author affiliation: Libr., Shandong Univ. of Technol., Zibo (Xiaoyue Wang; Zhen Hua; Rujiang Bai)
Abstract	Automatic categorization of documents into pre-defined taxonomies is a crucial step in data mining and knowledge discovery. Standard machine learning techniques like support vector machines(SVM) and related large margin methods have been successfully applied for this task. Unfortunately, the high dimensionality of input feature vectors impacts on the classification speed. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this work is to reduce the dimension of feature vectors, optimizing the parameters to improve the SVM classification accuracy and speed. In order to improve classification speed we spent rough sets theory to reduce the feature vector space. We present a genetic algorithm approach for feature selection and parameters optimization to improve classification accuracy. Experimental results indicate our method is more effective than traditional SVM methods and other traditional methods.
Starting Page	971
Ending Page	977
File Size	391604
Page Count	7
File Format	PDF
ISBN	9780769532639
DOI	10.1109/SNPD.2008.142
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-06
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Support vector machine classification Rough Sets Genetics Classification algorithms Support Vector Machine Kernel Genetic Algorithms Biological cells Document Classification
Content Type	Text
Resource Type	Article

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