Probabilistic reasoning in intelligent systems: networks of plausible inference
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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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A survey of interestingness measures for knowledge discovery
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Engineering Applications of Artificial Intelligence
Assessing the Quality of Rules with a New Monotonic Interestingness Measure Z
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Multicriteria Attractiveness Evaluation of Decision and Association Rules
Transactions on Rough Sets X
Properties of rule interestingness measures and alternative approaches to normalization of measures
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Fundamenta Informaticae - Machine Learning in Bioinformatics
Analysis of symmetry properties for bayesian confirmation measures
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
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The paper focuses on Bayesian confirmation measures used for evaluation of rules induced from data. To distinguish between many confirmation measures, their properties are analyzed. The article considers a group of symmetry properties. We demonstrate that the symmetry properties proposed in the literature focus on extreme cases corresponding to entailment or refutation of the rule's conclusion by its premise, forgetting intermediate cases. We conduct a thorough analysis of the symmetries regarding that the confirmation should express how much more probable the rule's hypothesis is when the premise is present rather than when the negation of the premise is present. As a result we point out which symmetries are desired for Bayesian confirmation measures. Next, we analyze a set of popular confirmation measures with respect to the symmetry properties and other valuable properties, being monotonicity M, Ex1 and weak Ex1, logicality L and weak L. Our work points out two measures to be the most meaningful ones regarding the considered properties.