Paper Info
Title
An extended transformation approach to inductive logic programming
Abstract
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. Propositionalization is a particular transformation method, in which the ILP task is compiled to an attribute-value learning task. The main restriction of propositionalization methods such as LINUS is that they are unable to deal with nondeterminate 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-centered 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.
Year
DOI
Venue
2001
10.1145/383779.383781
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Keywords
DocType
Volume
ilp domain,data mining,systematic first-order feature construction,ilp task,inductive logic programming,particular individual-centred feature bias,additional key words and phrases: data mining,extended transformation approach,particular individual-centered feature bias,particular transformation method,relational databases,machine learning,clear notion,transformation approach,exhaustive first-order feature construction,logic program
Journal
2
Issue
Citations 
PageRank 
4
41
2.14
References 
Authors
46
2
Name
Order
Citations
PageRank
Nada Lavrač198972.19
Peter A. Flach23457269.66