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dc.creatorAlexessander Alves
dc.creatorRui Camacho
dc.creatorEugénio Oliveira
dc.description.abstractThis paper addresses the problem of data mining in Inductive Logic Programming (ILP) motivated by its application in the domain of economics. ILP systems have been largely applied to data mining classification tasks with a considerable success. The use of ILP systems in regression tasks has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the application of ILP to discovery of functional relationships of numeric nature. This paper proposes improvements in numerical reasoning capabilities of ILP systems for dealing with regression tasks. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium inspired in the PAC learning framework. We have found these extensions essential to improve on results over machine learning and statistical-based algorithms used in the empirical evaluation study.
dc.relation.ispartofProceedings of the 2nd International Workshop on Data Mining and Adaptive Modelling Methods for Economics and Management
dc.subjectEngenharia electrotécnica, electrónica e informática
dc.subjectElectrical engineering, Electronic engineering, Information engineering
dc.titleInductive logic programming for data mining in economics
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Engenharia
dc.subject.fosCiências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fosEngineering and technology::Electrical engineering, Electronic engineering, Information engineering
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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