Machine intelligence 12
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Machine Learning
Diverse confidence levels in a probabilistic semantics for conditional logics
Artificial Intelligence
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Machine Learning
On the Proper Definition of Minimality in Specialization and Theory Revision
ECML '93 Proceedings of the European Conference on Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning Horn Expressions with LogAn-H
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
ACM SIGKDD Explorations Newsletter
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Coping with exceptions in multiclass ILP problems using possibilistic logic
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Efficient and effective induction of first order decision lists
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Learnability of description logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Possibilistic inductive logic programming
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
In this paper we propose a new formalization of the inductive logic programming (ILP) problem for a better handling of exceptions. It is now encoded in first-order possibilistic logic. This allows us to handle exceptions by means of prioritized rules, thus taking lessons from non-monotonic reasoning. Indeed, in classical first-order logic, the exceptions of the rules that constitute a hypothesis accumulate and classifying an example in two different classes, even if one is the right one, is not correct. The possibilistic formalization provides a sound encoding of non-monotonic reasoning that copes with rules with exceptions and prevents an example to be classified in more than one class. The benefits of our approach with respect to the use of first-order decision lists are pointed out. The possibilistic logic view of ILP problem leads to an optimization problem at the algorithmic level. An algorithm based on simulated annealing that in one turn computes the set of rules together with their priority levels is proposed. The reported experiments show that the algorithm is competitive to standard ILP approaches on benchmark examples.