Modeling Machine Learning and Data Mining Problems with FO(*)

Abstract : This paper reports on the use of the FO(*) language and the IDP framework for modeling and solving some machine learning and data mining tasks. The core component of a model in the IDP framework is an FO(*) theory consisting of formulas in first order logic and definitions; the latter are basically logic programs where clause bodies can have arbitrary first order formulas. Hence, it is a small step for a well-versed computer scientist to start modeling. We describe some models resulting from the collaboration between IDP experts and domain experts solving machine learning and data mining tasks. A first task is in the domain of stemmatology, a domain of philology concerned with the relationship between surviving variant versions of text. A second task is about a somewhat similar problem within biology where phylogenetic trees are used to represent the evolution of species. A third and final task is about learning a minimal automaton consistent with a given set of strings. For each task, we introduce the problem, present the IDP code and report on some experiments.
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Communication dans un congrès
The 28th International Conference on Logic Programming (ICLP'12), Sep 2012, Hungary. 17, pp.14-25, 2012, Leibniz International Proceedings in Informatics
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Dernière modification le : jeudi 13 septembre 2012 - 10:35:46
Document(s) archivé(s) le : vendredi 16 décembre 2016 - 12:50:50

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Hendrik Blockeel, Bart Bogaerts, Maurice Bruynooghe, Broes De Cat, Stef De Pooter, et al.. Modeling Machine Learning and Data Mining Problems with FO(*). The 28th International Conference on Logic Programming (ICLP'12), Sep 2012, Hungary. 17, pp.14-25, 2012, Leibniz International Proceedings in Informatics. <hal-00731459>

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