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Multiple labels associative classification

Thabtah, Fadi Abdeljaber, Cowling, Peter and Peng, Yonghong (2006) Multiple labels associative classification. Knowledge and Information Systems, 9 (1). pp. 109-129. ISSN 0219-1377

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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 can produce more efficient and accurate classifiers than traditional techniques. In this paper, the problem of producing rules with multiple labels is investigated, and we propose a multi-class, multi-label associative classification approach (MMAC). In addition, four measures are presented in this paper for evaluating the accuracy of classification approaches to a wide range of traditional and multi-label classification problems. Results for 19 different data sets from the UCI data collection and nine hyperheuristic scheduling runs show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches.

    Item Type: Article
    Additional Information: UoA 23 (Computer Science and Informatics) © Springer-Verlag London Ltd. 2005
    Subjects: Q Science > Q Science (General)
    Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Schools: School of Computing and Engineering

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    Depositing User: Sara Taylor
    Date Deposited: 12 Oct 2007
    Last Modified: 28 Jul 2010 19:21

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