Abstract
Separate-and-conquer classifiers strongly depend on the criteria used to choose which rules will be included in the classification model. When association rules are employed to build such classifiers (as in ART [3]), rule evaluation can be performed attending to different criteria (other than the traditional confidence measure used in association rule mining). In this paper, we analyze the desirable properties of such alternative criteria and their effect in building rule-based classifiers using a separate-and-conquer strategy.
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Berzal, F., Cubero, JC., Marín, N., Polo, JL. (2006). An Overview of Alternative Rule Evaluation Criteria and Their Use in Separate-and-Conquer Classifiers. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_66
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DOI: https://doi.org/10.1007/11875604_66
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