A Concept Learning Tool Based On Calculating Version Space Cardinality
نویسندگان
چکیده
In this paper, we proposed VeSC-CoL (Version Space Cardinality based Concept Learning) to deal with concept learning on extremely imbalanced datasets, especially when crossvalidation is not a viable option. VeSC-CoL uses version space cardinality as a measure for model quality to replace cross-validation. Instead of naive enumeration of the version space, Ordered Binary Decision Diagram and Boolean Satisfiability are used to compute the version space. Experiments show that VeSCCoL can accurately learn the target concept when computational resource is allowed.
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