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

Sheydaei, N, Saraee, MH and Shahgholian, A 2015, 'A novel feature selection method for text classification using association rules and clustering' , Journal of Information Science, 41 (1) , pp. 3-15.

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Readability and accuracy are two important features of a good classifier. For reasons such as acceptable accuracy, rapid training and high interpretability, associative classifiers have been recently used in many categorization tasks. These features could be very useful in text classification, however both training time and the number of produced rules will increase significantly due to the high dimensionality of text documents. In this paper an association classification algorithm for text classification is proposed which includes a feature selection phase to select important features and a clustering phase based on class labels to tackle this short-coming. 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 performance.

Item Type: Article
Themes: Memory, Text and Place
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Journal of Information Science
Publisher: SAGE
Refereed: Yes
ISSN: 0165-5515
Related URLs:
Funders: Non funded research
Depositing User: Dr Mo Saraee
Date Deposited: 24 Feb 2015 12:31
Last Modified: 30 Nov 2015 11:11

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