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A novel feature selection method for text classification using association rules and clustering

Sheydaei, N, Saraee, M and Shahgholian, A (2014) A novel feature selection method for text classification using association rules and clustering. Journal of Information Science, 41 (1). pp. 3-15. ISSN 0165-5515

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Abstract

Readability and accuracy are two important features of any good classifier. For reasons such as acceptable accuracy, rapid training and high interpretability, associative classifiers have recently been used in many categorization tasks. Although features could be very useful in text classification, both training time and the number of produced rules will increase significantly owing to the high dimensionality of text documents. In this paper an association classification algorithm for text classification is proposed that includes a feature selection phase to select important features and a clustering phase based on class labels to tackle this shortcoming. The experimental results from applying the proposed algorithm in comparison with the results of selected well-known classification algorithms show that our approach outperforms others both in efficiency and in performance.

Item Type: Article
Uncontrolled Keywords: 08 Information And Computing Sciences
Subjects: H Social Sciences > HF Commerce > HF5001 Business
Divisions: Liverpool Business School
Publisher: Sage
Related URLs:
Date Deposited: 27 Jun 2019 08:23
Last Modified: 27 Jun 2019 08:23
DOI or Identification number: 10.1177/0165551514550143
URI: http://researchonline.ljmu.ac.uk/id/eprint/9448

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