Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Linear Constraints in Inductive Logic Programming
ECML '97 Proceedings of the 9th European Conference on Machine Learning
A Coevolutionary Approach to Concept Learning
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Generating Numerical Literals During Refinement
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Learning Structurally Indeterminate Clauses
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Searching the Subsumption Lattice by a Genetic Algorithm
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Search-intensive concept induction
Evolutionary Computation
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Good and bad practices in propositionalisation
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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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.