Abstract
In logical analysis of data, the feature selection step only considers selecting minimal number of features after binarization. This paper develops a nonlinear set covering model that explores the interaction between the number of selected original attributes and binarized features. Utilizing the partial derivative of pseudo-Boolean functions, we give a greedy heuristic for the nonlinear set covering problem. The efficacy of the algorithm is demonstrated through experiments on 10 public machine learning datasets from UCI repository.
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