A blended feature selection method in text classification

In text classification system, accuracy is a major indicator of performance, and feature selection method has a significant impact on it. In this paper, we propose a blended feature selection method, which combines four traditional feature selection methods (document frequency, information gain, mut...

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Bibliographic Details
Published inIET Conference Proceedings pp. 577 - 580
Main Authors Shen, Kewei, Chen, Xian, Ma, Jing, Le, Ke, Lu, Yueming, Zhang, Kuo
Format Conference Proceeding
LanguageEnglish
Published Stevenage, UK IET 2013
The Institution of Engineering & Technology
Subjects
Business and administrative computing
Computer networks and techniques
Cryptography
Data handling techniques
Digital signal processing
General and management topics
General electrical engineering topics
Information analysis and indexing
Information technology applications
Internet software
Management and business
Optical fibre networks
Optical, image and video signal processing
Radio links and equipment
Robot and manipulator mechanics
Robotics
Telecommunication applications
image processing
cyberspace technology
text analysis
signal processing
optical networks
computer networks
multimedia systems
distributed processing
cryptography
multimedia
mobile communication
optical fibre networks
robots
cloud computing
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Summary:In text classification system, accuracy is a major indicator of performance, and feature selection method has a significant impact on it. In this paper, we propose a blended feature selection method, which combines four traditional feature selection methods (document frequency, information gain, mutual information and chi-square) into a better feature selection method. BFSM is tested on a Chinese corpus of 4000 documents and improves the accuracy compared with those traditional methods.
ISBN:9781849198011
1849198012
DOI:10.1049/cp.2013.2077