Abstract
IFPD confirmation measures are used in ranking inductive rules in Data Mining. Many measures of this kind have been defined in literature. We show how some of them are related to each other via weighted means. The special structure of IFPD measures allows to define also new monotonicity and symmetry properties which appear quite natural in such context. We also suggest a way to measure the degree of symmetry of IFPD confirmation measures.
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Notes
- 1.
As a matter of fact, when the evidence E disconfirms conclusion H, i.e. \(P(H|E)<P(H)\), both \(Z_1\) and \(Z_2\) assume a negative value: strictly speaking their harmonic mean is not defined, but the proposed link (1) among measures holds, with the same meaning. In the neutrality case we have the boundary values \(K=Z=0\) and their link cannot be defined by a harmonic mean like (1).
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Celotto, E., Ellero, A., Ferretti, P. (2016). Monotonicity and Symmetry of IFPD Bayesian Confirmation Measures. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2016. Lecture Notes in Computer Science(), vol 9880. Springer, Cham. https://doi.org/10.1007/978-3-319-45656-0_10
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