Climbing the Hill with ILP to Grow Patterns in Fuzzy Tensors

Abstract

Fuzzy tensors encode to what extent n-ary predicates are satisfied. The disjunctive box cluster model is a regression model where sub-tensors are explanatory variables for the values in the fuzzy tensor. In this article, locally optimal patterns for that model, with high areas times squared densities, are grown by hill-climbing from fragments of them. A forward selection then chooses among the discovered patterns a non-redundant subset that fits, but does not overfit, the tensor.

Keywords

• Disjunctive box cluster model
• Fuzzy tensor
• Hill-climbing
• Integer Linear Programming
• Forward selection