W-operator window design by maximization of training data information

This paper presents a technique that gives a minimal window W for the estimation of a W-operator from training data. The idea is to choose a subset of variables W that maximizes the information observed in a set of training data. The task is formalized as a combinatorial optimization problem, where the search space is the powerset of the candidate variables and the measure to be minimized is the mean entropy of the estimated conditional probabilities. As a full exploration of the search space requires an enormous computational effort, some heuristics of the feature selection literature are applied. The proposed technique is mathematically sound and experimental results show that it is adequate in practice.