Article: Positive and negative generic classification rules-based classifier Journal: International Journal of Knowledge and Learning (IJKL) 2011 Vol.7 No.3/4 pp.271 - 293 Abstract: Associative classification is a supervised classification method. Many experimental studies have shown that associative classification is a promising approach. However, the latter suffer from a major drawback: the huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose in this paper a new direct associative classification method called IGARC, an improvement of GARC approach that extracts directly generic associative classification rules from a training set in order to reduce the number of associative classification rules without jeopardising the classification accuracy. Moreover, we propose an algorithm called PN-GARC that deals with negative classification rules. Considering negated items in classification framework provides additional information describing the data and reduces the conflicts while classifying new objects. Nevertheless, there are a sheer number of rules when considering negated items. That is why, we will explore generic classification rules both negative and positive ones in order to study their behaviour and their usefulness on the studied datasets. A detailed description of IGARC method is presented, as well as the experimentation study on 12 benchmark datasets proving that it is highly competitive in terms of accuracy in comparison with popular classification approaches. Inderscience Publishers - linking academia, business and industry through research

Title: Positive and negative generic classification rules-based classifier

Authors: Ines Bouzouita; Samir Elloumi

Addresses: Faculty of Sciences of Tunis, Computer Science Department, 1060 Tunis, Tunisia. ' Faculty of Sciences of Tunis, Computer Science Department, 1060 Tunis, Tunisia

Abstract: Associative classification is a supervised classification method. Many experimental studies have shown that associative classification is a promising approach. However, the latter suffer from a major drawback: the huge number of the generated classification rules which takes efforts to select the best ones in order to construct the classifier. To overcome such drawback, we propose in this paper a new direct associative classification method called IGARC, an improvement of GARC approach that extracts directly generic associative classification rules from a training set in order to reduce the number of associative classification rules without jeopardising the classification accuracy. Moreover, we propose an algorithm called PN-GARC that deals with negative classification rules. Considering negated items in classification framework provides additional information describing the data and reduces the conflicts while classifying new objects. Nevertheless, there are a sheer number of rules when considering negated items. That is why, we will explore generic classification rules both negative and positive ones in order to study their behaviour and their usefulness on the studied datasets. A detailed description of IGARC method is presented, as well as the experimentation study on 12 benchmark datasets proving that it is highly competitive in terms of accuracy in comparison with popular classification approaches.

Keywords: associative classification; generic classification rules; negative classification rules; rules-based classifiers.

DOI: 10.1504/IJKL.2011.044562

International Journal of Knowledge and Learning, 2011 Vol.7 No.3/4, pp.271 - 293

Received: 30 Nov 2010
Accepted: 15 Sep 2011
Published online: 31 Jan 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Positive and negative generic classification rules-based classifier

Keep up-to-date