The Generic Rough Set Inductive Logic Programming (gRS—ILP) Model
Arul Siromoney, Katsushi Inoue · Studies in fuzziness and soft computing · 2002
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. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.