Inductive Logic Programming (ILP) is concerned with learning relational descriptions that typically have the form of logic programs. In a transformation approach, an ILP task is transformed into an equivalent learning task in a different representation formalism. Propositionalisation is a particular transformation method, in which the ILP task is compiled down to an attribute-value learning task. The main restriction of propositionalisation methods such a s LINUS is that they are unable to deal with non-determinate local variables in the body of hypothesis clauses. In this paper we show how this limitation can be overcome, by systematic first-order feature construction using a particular individual-centred feature bias. The approach can be applied in any domain where there is a clear notion of individual. We also show how to improve upon exhaustive first-order feature construction by using a relevancy filter. The proposed approach is illustrated on the ``trains'' and ``mutagenesis'' ILP domains.
|Translated title of the contribution||An extended transformation approach to Inductive Logic Programming|
|Publisher||Department of Computer Science, University of Bristol|
|Publication status||Published - 2000|
Bibliographical noteOther page information: -42
Other identifier: 1000442