Abstract
Rules mined from a data set represent knowledge patterns relating premises and decisions in ’if ..., then ...’ statements. Premise is a conjunction of elementary conditions relative to independent variables and decision is a conclusion relative to dependent variables. Given a set of rules, it is interesting to rank them with respect to some attractiveness measures. In this paper, we are considering rule attractiveness measures related to three semantics: knowledge representation, prediction and efficiency of intervention based on a rule. Analysis of existing measures leads us to a conclusion that the best suited measures for the above semantics are: support and certainty, a Bayesian confirmation measure, and two measures related to efficiency of intervention, respectively. These five measures induce a partial order in the set of rules. For building a strategy of intervention, we propose rules discovered using the Dominance-based Rough Set Approach – the “at least” type rules indicate opportunities for improving assignment of objects, and the “at most” type rules indicate threats for deteriorating assignment of objects.
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Słowiński, R., Greco, S. (2005). Measuring Attractiveness of Rules from the Viewpoint of Knowledge Representation, Prediction and Efficiency of Intervention. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds) Advances in Web Intelligence. AWIC 2005. Lecture Notes in Computer Science(), vol 3528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11495772_3
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DOI: https://doi.org/10.1007/11495772_3
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