A Concept Learning Tool Based On Calculating Version Space Cardinality
  In this paper, we proposed VeSC-CoL (Version Space Cardinality based Concept
Learning) to deal with concept learning on extremely imbalanced datasets,
especially when cross-validation 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 VeSC-CoL can accurately learn the target concept when
computational resource is allowed.
