Abstract
Associative classification is a well-known technique for structured data classification. Most previous work on associative classification based the assignment of the class label on a single classification rule. In this work we propose the assignment of the class label based on simple majority voting among a group of rules matching the test case.
We propose a new algorithm,\(L_M^3\), which is based on previously proposed algorithm L 3. L 3 M performed a reduced amount of pruning, coupled with a two step classification process. \(L_M^3\) combines this approach with the use of multiple rules for data classification. The use of multiple rules, both during database coverage and classification, yields an improved accuracy.
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Baralis, E., Garza, P. (2003). Majority Classification by Means of Association Rules. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_6
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DOI: https://doi.org/10.1007/978-3-540-39804-2_6
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