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

Author:

Sheydaei Navid1,Saraee Mohamad2,Shahgholian Azar1

Affiliation:

1. Department of Computer Engineering, Isfahan University of Technology, Iran

2. School of Computing, Science and Engineering, University of Salford-Manchester, UK

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.

Publisher

SAGE Publications

Subject

Library and Information Sciences,Information Systems

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