New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
A machine discovery from amino acid sequences by decision trees over regular patterns
Selected papers of international conference on Fifth generation computer systems 92
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
The Generic Rough Set Inductive Logic Programming Model and Motifs in Strings
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
The Variable Precision Rough Set Inductive Logic Programming Model and Web Usage Graphs
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Learning First-Order Rules: A Rough Set Approach
Fundamenta Informaticae - International Conference on Soft Computing and Distributed Processing (SCDP'2002)
Tolerance rough set-inductive logic programming (RS-ILP)
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Studies on rough sets in multiple tables
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Learning First-Order Rules: A Rough Set Approach
Fundamenta Informaticae - International Conference on Soft Computing and Distributed Processing (SCDP'2002)
Hi-index | 0.00 |
The example semantics of Inductive Logic Programming (ILP) systems is said to be in a rough setting when the consistency and completeness criteria cannot both be fulfilled together, because the evidence, background knowledge and declarative bias are such that any induced hypothesis cannot distinguish between some of the positive and negative examples. The gRS-ILP model (generic Rough Set Inductive Logic Programming model) provides a theoretical foundation in this rough setting for an ILP system to induce hypotheses that are used to say that an example is definitely positive, or definitely negative. An illustrative example using Progol is presented. Results are presented of GOLEM experiments using the data set for drug design for Alzheimer's disease and other experiments using Progol on mutagenesis data and transmembrane domain data.