Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An assessment of submissions made to the Predictive Toxicology Evaluation Challenge
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Involving Aggregate Functions in Multi-relational Search
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Context-sensitive refinements for stochasticoptimization algorithms in inductive logic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming
Artificial Intelligence Review
Refining aggregate conditions in relational learning
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A comparison of approaches for learning probability trees
ECML'05 Proceedings of the 16th European conference on Machine Learning
Classifying relational data with neural networks
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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The Predictive Toxicology Evaluation (or PTE) Challenge provided Machine Learning techniques with the opportunity to compete against specialised techniques for toxicology prediction. Toxicity models that used findings from ILP programs have performed creditably in the PTE-2 experiment proposed under this challenge. We report here on an assessment of such models along scales of: (1) quantitative performance, in comparison to models developed with expert collaboration; and (2) potential explanatory value for toxicology. Results appear to suggest the following: (a) across of range of class distributions and error costs, some explicit models constructed with ILP-assistance appear closer to optimal than most expert-assisted ones. Given the paucity of test-data, this is to be interpreted cautiously; (b) a combined use of propositional and ILP techniques appears to yield models that contain unusual combinations of structural and biological features; and (c) significant effort was required to interpret the output, strongly indicating the need to invest greater effort in transforming the output into a "toxicologist-friendly" form. Based on the lessons learnt from these results, we propose a new predictive toxicology evaluation experiment - PTE-3 - which will address some important shortcomings of the previous study.