eprintid: 1005
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/10/05
datestamp: 2023-11-09 15:49:09
lastmod: 2023-11-09 15:49:09
status_changed: 2023-11-09 15:38:51
type: article
metadata_visibility: show
creators_name: Zaiyadi, M.F.
creators_name: Baharudin, B.
title: A proposed hybrid approach for feature selection in text document categorization
ispublished: pub
keywords: Ant-colony optimization; Feature selection; Information gain; Text categorization; Text representation, Algorithms; Artificial intelligence; Optimization; Text processing, Feature extraction
note: cited By 1
abstract: Text document categorization involves large amount of data or features. The high dimensionality of features is a troublesome and can affect the performance of the classification. Therefore, feature selection is strongly considered as one of the crucial part in text document categorization. Selecting the best features to represent documents can reduce the dimensionality of feature space hence increase the performance. There were many approaches has been implemented by various researchers to overcome this problem. This paper proposed a novel hybrid approach for feature selection in text document categorization based on Ant Colony Optimization (ACO) and Information Gain (IG). We also presented state-of-the-art algorithms by several other researchers.
date: 2010
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651593676&partnerID=40&md5=c59ed4f89ed18c5bff83ca741a230819
full_text_status: none
publication: World Academy of Science, Engineering and Technology
volume: 72
pagerange: 137-141
refereed: TRUE
issn: 2010376X
citation:   Zaiyadi, M.F. and Baharudin, B.  (2010) A proposed hybrid approach for feature selection in text document categorization.  World Academy of Science, Engineering and Technology, 72.  pp. 137-141.  ISSN 2010376X