On Learning with LAD
C. A. Jothishwaran Biplav Srivastava Jitin Singla Sugata Gangopadhyay
The logical analysis of data, LAD, is a technique that yields two-class
classifiers based on Boolean functions having disjunctive normal form (DNF)
representation. Although LAD algorithms employ optimization techniques, the
resulting binary classifiers or binary rules do not lead to overfitting. We
propose a theoretical justification for the absence of overfitting by
estimating the Vapnik-Chervonenkis dimension (VC dimension) for LAD models
where hypothesis sets consist of DNFs with a small number of cubic monomials.
We illustrate and confirm our observations empirically.