A blended feature selection method in text classification

• Published in: IET Conference Proceedings pp. 577 - 580
• Main Authors: Shen, Kewei, Chen, Xian, Ma, Jing, Le, Ke, Lu, Yueming, Zhang, Kuo
• Format: Conference Proceeding
• Language: English
• 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
• DOI: 10.1049/cp.2013.2077
• ISBN: 9781849198011; 1849198012

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.