DocumentCode :
603745
Title :
A fuzzy self-constructing algorithm for feature reduction
Author :
Chavali, A. ; Kulkarni, A.D.
Author_Institution :
Comput. Sci. Dept., Univ. of Texas at Tyler, Tyler, TX, USA
fYear :
2013
fDate :
11-11 March 2013
Firstpage :
35
Lastpage :
40
Abstract :
The main aim of text categorization is the classification of documents into a fixed number of predefined categories. In text categorization, the dimensionality of the feature vector is usually high. Various approaches have been proposed to reduce the dimensionality of the feature vector while performing automatic text categorization. This paper deals a fast fuzzy self-constructing algorithm that reduces the dimensionality of a feature vector. We also perform automatic categorization of text and hypertext documents using a Support Vector Machines (SVMs) classifier. AS an illustrative example, we considered a set of documents with 15 documents with up to 30 feature words. A fuzzy self-constructing algorithm was used to obtain the reduced number of features. During the training phase the SVM classifier was trained using the reduced set of features. During the decision making phase the SVM classifier was used to classify unknown documents.
Keywords :
decision making; fuzzy set theory; pattern classification; support vector machines; text analysis; SVM classifier; decision making phase; document classification; feature reduction; feature vector dimensionality; fuzzy self-constructing algorithm; hypertext documents; support vector machines classifier; text categorization; training phase; Classification algorithms; Clustering algorithms; Feature extraction; Support vector machine classification; Text categorization; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Theory (SSST), 2013 45th Southeastern Symposium on
Conference_Location :
Waco, TX
ISSN :
0094-2898
Print_ISBN :
978-1-4799-0037-4
Type :
conf
DOI :
10.1109/SSST.2013.6524958
Filename :
6524958
Link To Document :
بازگشت