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MMAC: A New Multi-Class, Multi-Label Associative Classification Approach

Thabtah, Fadi, Cowling, Peter and Peng, Yonghong (2004) MMAC: A New Multi-Class, Multi-Label Associative Classification Approach. In: Data Mining, 2004. ICDM '04. Fourth IEEE International Conference, 1-4 November 2004.

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Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is growing evidence that integrating classification and association rule mining together can produce more efficient and accurate classifiers than traditional classification techniques. In this paper, the problem of producing rules with multiple labels is investigated. We propose a new associative classification approach called multi-class, multi-label associative classification (MMAC). This paper also presents three measures for evaluating the accuracy of data mining classification approaches to a wide range of traditional and multi-label classification problems. Results for 28 different datasets show that the MMAC approach is an accurate and effective classification technique, highly competitive and scalable in comparison with other classification approaches.

Item Type: Conference or Workshop Item (Paper)
Additional Information: UoA 23 (Computer Science and Informatics)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
Depositing User: Graham Stone
Date Deposited: 22 Oct 2008 09:59
Last Modified: 28 Aug 2021 10:42


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