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An ILP model for a monotone graded classification problem

Vojtáš, Peter; Horvath, Tomas; Krajči, Stanislav; Lencses, Rastislav


Peter Vojtáš

Tomas Horvath

Stanislav Krajči

Rastislav Lencses


Motivation for this paper are classification problems in which data can not be clearly divided into positive and negative examples, especially data in which there is a monotone hierarchy (degree, preference) of more or less positive (negative) examples. We present a new formulation of a fuzzy inductive logic programming task in the framework of fuzzy logic in narrow sense. Our construction is based on a syntactical equivalence of fuzzy logic programs FLP and a restricted class of generalised annotated programs. The induction is achieved via multiple use of classical two valued induction on α
-cuts of fuzzy examples with monotonicity axioms in background knowledge, which is afterwards again glued together to a single annotated hypothesis. Correctness of our method (translation) is based on the correctness of FLP. The cover relation is based on fuzzy Datalog and fixpoint semantics for FLP. We present and discuss results of ILP systems GOLEM and ALEPH on illustrative examples. We comment on relations of our results to some statistical models and Bayesian logic programs.


Vojtáš, P., Horvath, T., Krajči, S., & Lencses, R. (2004). An ILP model for a monotone graded classification problem. Kybernetika, 40(3), 317-332

Journal Article Type Conference Paper
Conference Name Znalosti 2003
Conference Location Ostrava, Czech Republic
Online Publication Date Mar 1, 2004
Publication Date 2004
Deposit Date Apr 2, 2024
Print ISSN 0023-5954
Publisher The Institute of Information Theory and Automation (UTIA)
Peer Reviewed Peer Reviewed
Volume 40
Issue 3
Pages 317-332
Keywords graded classification, ILP, annotated programs
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