Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/540
Author(s): Alexessander Alves
Rui Carlos Camacho de Sousa Ferreira da Silva
Eugénio da Costa Oliveira
Title: Learning time series models with inductive logic programming
Issue Date: 2003
Abstract: : This paper reports on a set of proposals that make Inductive Logic Programming (ILP) systems adequatefor inducing time series models.The proposals include an improvement in the ILP search process by the introduction of a statistical model validationstep. We propose the definition of an adequate cost function based on the information criteria. The definition of themodel evaluation step consists in an intuitive statistics that limits the minimum accepted performance of an inducedhypothesis. The ILP system we used was provided with a library of background knowledge predicates adequate for timeseries problems.The proposals described in this paper can be applied to any agnostic learning problem.Preliminary experiments have shown that all these modifications make an ILP system adequate to induce time seriesmodels and increase the capability of model choice automation.
Subject: Informática, Engenharia electrotécnica, electrónica e informática
Informatics, Electrical engineering, Electronic engineering, Information engineering
Scientific areas: Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
URI: https://repositorio-aberto.up.pt/handle/10216/540
Source: Proceedings de EUROPEAN SYMPOSIUM ON INTELLIGENT TECHNOLOGIES, HYBRID SYSTEMS AND THEIR IMPLEMENTATION ON SMART ADAPTIVE SYSTEMS, EUNITE 2003
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: restrictedAccess
License: https://creativecommons.org/licenses/by-nc/4.0/
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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