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
Extracting Context-Sensitive Models in Inductive Logic Programming
Machine Learning
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
ECML '93 Proceedings of the European Conference on Machine Learning
An assessment of submissions made to the Predictive Toxicology Evaluation Challenge
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Stochastic Logic Programs: Sampling, Inference and Applications
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Strategies to parallelize ILP systems
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
ILP meets knowledge engineering: a case study
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Common sense reasoning – from cyc to intelligent assistant
Ambient Intelligence in Everyday Life
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Inductive Logic Programming (ILP) systems construct models for data using domain-specific background information. When using these systems, it is typically assumed that sufficient human expertise is at hand to rule out irrelevant background information. Such irrelevant information can, and typically does, hinder an ILP system's search for good models. Here, we provide evidence that if expertise is available that can provide a partial-ordering on sets of background predicates in terms of relevance to the analysis task, then this can be used to good effect by an ILP system. In particular, using data from biochemical domains, we investigate an incremental strategy of including sets of predicates in decreasing order of relevance. Results obtained suggest that: (a) the incremental approach identifies, in substantially less time, a model that is comparable in predictive accuracy to that obtained with all background information in place; and (b) the incremental approach using the relevance ordering performs better than one that does not (that is, one that adds sets of predicates randomly). For a practitioner concerned with use of ILP, the implication of these findings are two-fold: (1) when not all background information can be used at once (either due to limitations of the ILP system, or the nature of the domain) expert assessment of the relevance of background predicates can assist substantially in the construction of good models; and (2) good "first-cut" results can be obtained quickly by a simple exclusion of information known to be less relevant.