Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Refining Complete Hypotheses in ILP
ILP '99 Proceedings of the 9th 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
Inference for the Generalization Error
Machine Learning
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Relational learning as search in a critical region
The Journal of Machine Learning Research
QG/GA: a stochastic search for Progol
Machine Learning
Genetic local search for rule learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
ILP Through Propositionalization and Stochastic k-Term DNF Learning
Inductive Logic Programming
TopLog: ILP Using a Logic Program Declarative Bias
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Lattice-search runtime distributions may be heavy-tailed
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Using Bayesian networks to direct stochastic search in inductive logic programming
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Revising first-order logic theories from examples through stochastic local search
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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Genetic Algorithms (GAs) are known for their capacity to explore large search spaces and due to this ability they were applied (to some extent) to Inductive Logic Programming (ILP). Although Estimation of Distribution Algorithms (EDAs) generally perform better than standard GAs, they have not been applied to ILP. This work presents EDA-ILP, an ILP system based on EDA and inverse entailment, and also its extension, the REDA-ILP, which employs the Reduce algorithm in bottom clauses to considerably reduce the search space. Experiments in real-world datasets showed that both systems were successfully compared to Aleph and GA-ILP (another variant of EDA-ILP created replacing the EDA by a standard GA). EDA-ILP was also successfully compared to Progol-QG/GA (and its other variants) in phase transition benchmarks. Additionally, we found that REDA-ILP usually obtains simpler theories than EDA-ILP, more efficiently and with equivalent accuracies. These results show that EDAs provide a good base for stochastic search in ILP.