A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Refining Numerical Constants in First Order Logic Theories
Machine Learning - Special issue on multistrategy learning
Generating Numerical Literals During Refinement
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Quantitative pharmacophore models with inductive logic programming
Machine Learning
An integrated approach to feature invention and model construction for drug activity prediction
Proceedings of the 24th international conference on Machine learning
A Real generalization of discrete AdaBoost
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Maximum Common Subgraph based locally weighted regression
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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We present a numerical refinement operator based on multiinstance learning. In the approach, the task of handling numerical variables in a clause is delegated to statistical multi-instance learning schemes. To each clause, there is an associated multi-instance classification model with the numerical variables of the clause as input. Clauses are built in a greedy manner, where each refinement adds new numerical variables which are used additionally to the numerical variables already known to the multi-instance model. In our experiments, we tested this approach with multi-instance learners available in the Weka workbench (like MISVMs). These clauses are used in a boosting approach that can take advantage of the margin information, going beyond standard covering procedures or the discrete boosting of rules, like in SLIPPER. The approach is evaluated on the problem of hexose binding site prediction, a pharmacological application and mutagenicity prediction. In two of the three applications, the task is to find configurations of points with certain properties in 3D space that characterize either a binding site or drug activity: the logical part of the clause constitutes the points with their properties, whereas the multi-instance model constrains the distances among the points. In summary, the new numerical refinement operator is interesting both theoretically as a new synthesis of logical and statistical learning and practically as a new method for characterizing binding sites and pharmacophores in biochemical applications.