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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Published in | IET Conference Proceedings pp. 577 - 580 |
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Main Authors | , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
Stevenage, UK
IET
2013
The Institution of Engineering & Technology |
Subjects | |
Online Access | Get full text |
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Abstract | 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. |
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AbstractList | 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. |
Author | Kuo Zhang Kewei Shen Jing Ma Le Ke Yueming Lu Xian Chen |
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Copyright | Copyright The Institution of Engineering & Technology Nov 23, 2013 |
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DOI | 10.1049/cp.2013.2077 |
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Snippet | 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... |
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SubjectTerms | 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 |
Title | A blended feature selection method in text classification |
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