A Concept Learning Tool Based On Calculating Version Space Cardinality

Kuo-Kai Hsieh; Li-C. Wang

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arXiv · 1803.08625

A Concept Learning Tool Based On Calculating Version Space Cardinality

Kuo-Kai Hsieh·Li-C. Wang

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Abstract

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.

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Kuo-Kai Hsieh, Li-C. Wang. 2018-03-23. A Concept Learning Tool Based On Calculating Version Space Cardinality. https://arxiv.org/abs/1803.08625

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