Inductive logic programming: derivations, successes and shortcomings
ACM SIGART Bulletin
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Program Improvement by Source-to-Source Transformation
Journal of the ACM (JACM)
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
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
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
An assessment of submissions made to the predictive toxicology evaluation challenge
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Application of Pruning Techniques for Propositional Learning to Progol
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Enhanced sharing analysis techniques: a comprehensive evaluation
Theory and Practice of Logic Programming
| Hi-index | 0.00 |
Inductive Logic Programming (ILP) systems have had noteworthy successes in extracting comprehensible and accurate models for data drawn from a number of scientific and engineering domains. These results suggest that ILP methods could enhance the model-construction capabilities of software tools being developed for the emerging discipline of "knowledge discovery from databases." One significant concern in the use of ILP for this purpose is that of efficiency. The performance of modern ILP systems is principally affected by two issues: (1) they often have to search through very large numbers of possible rules (usually in the form of definite clauses); (2) they have to score each rule on the data (usually in the form of ground facts) to estimate "goodness". Stochastic and greedy approaches have been proposed to alleviate the complexity arising from each of these issues. While these techniques can result in order-of-magnitude improvements in the worst-case search complexity of an ILP system, they do so at the expense of exactness. As this may be unacceptable in some situations, we examine two methods that result in admissible transformations of clauses examined in a search. While the methods do not alter the size ofthe search space (that is, the number of clauses examined), they can alleviate the theorem-proving effort required to estimate goodness. The first transformation simply involves eliminating literals using a weak test for redundancy. The second involves partitioning the set of literals within a clause into groups that can be executed independently of each other. The efficacy ofthe se transformations are evaluated empirically on a number of well-known ILP datasets. The results suggest that for problems that require the use of highly nondeterminate predicates, the transformations can provide significant gains as the complexity of clauses sought increases.