Thabtah, Fadi Abdeljaber, 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.Metadata only available from this repository.
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:||10 Jan 2017 13:40|
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