Nowadays, propositionalization is an important method that aims at reducing the complexity of Inductive Logic Programming, by transforming a learning problem expressed in a first order formalism into an attribute-value representation. This implies a two steps process, namely finding an interesting pattern and then learning relevant constraints for this pattern. This paper describes a novel genetic approach for handling the second task. The main idea of our approach is to consider the set of variables appearing in the pattern, and to learn a partition of this set. Numeric constraints are directly put on the equivalence classes involved by the partition rather than on variables. We have proposed an encoding for representing a partition by an individual, and general set-based operators to alter one partition or to mix two ones. For propositionalization, operators are extended to change not only the partition but also the associated numeric constraints.
CITATION STYLE
Braud, A., & Vrain, C. (2001). A genetic algorithm for propositionalization. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2157, pp. 27–40). Springer Verlag. https://doi.org/10.1007/3-540-44797-0_3
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