Communications of the ACM
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Machine Learning - special issue on inductive logic programming
ACM SIGKDD Explorations Newsletter
ECML '93 Proceedings of the European Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Demand-Driven Construction of Structural Features in ILP
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
Mathematical applications of inductive logic programming
Machine Learning
Lattice-search runtime distributions may be heavy-tailed
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
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Boosting is an established propositional learning method to promote the predictive accuracy of weak learning algorithms, and has achieved much empirical success. However, there have been relatively few efforts to apply boosting to Inductive Logic Programming (ILP) approaches. We investigate the use of boosting descriptive ILP systems, by proposing a novel algorithm for generating classification rules which searches using a hybrid language bias/production rule approach, and a new method for converting first-order classification rules to binary classifiers, which increases the predictive accuracy of the boosted classifiers. We demonstrate that our boosted approach is competitive with normal ILP systems in experiments with bioinformatics datasets.