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A greedy classification algorithm based on association rule

Thabtah, Fadi and Cowling, Peter (2007) A greedy classification algorithm based on association rule. Applied Soft Computing, 7 (3). pp. 1102-1111. ISSN 1568-4946

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Classification and association rule discovery are important data mining tasks. Using association rule discovery to construct classification systems, also known as associative classification, is a promising approach. In this paper, a new associative classification technique, Ranked Multilabel Rule (RMR) algorithm is introduced, which generates rules with multiple labels. Rules derived by current associative classification algorithms overlap in their training objects, resulting in many redundant and useless rules. However, the proposed algorithm resolves the overlapping between rules in the classifier by generating rules that does not share training objects during the training phase, resulting in a more accurate classifier. Results obtained from experimenting on 20 binary, multi-class and multi-label data sets show that the proposed technique is able to produce classifiers that contain rules associated with multiple classes. Furthermore, the results reveal that removing overlapping of training objects between the derived rules produces highly competitive classifiers if compared with those extracted by decision trees and other associative classification techniques, with respect to error rate

Item Type: Article
Additional Information: UoA 23 (Computer Science and Informatics) © 2006 Elsevier B.V.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
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Depositing User: Sara Taylor
Date Deposited: 02 Jul 2007
Last Modified: 28 Aug 2021 14:18


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