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

Hendrik Blockeel (1, 2) , Bart Bogaerts (1, 2) , Maurice Bruynooghe (1, 2) , Broes de Cat (2) , Stef de Pooter (2) , Marc Denecker (2) , Anthony Labarre (3) , Jan Ramon (2) , Sicco Verwer (4)

1 DTAI - Declarative Languages and Artificial Intelligence
2 KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
3 ULB - Université libre de Bruxelles
4 CS - Computing Science Department

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.

Domaines: Apprentissage [cs.LG]

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. pp.14-25. ⟨hal-00731459⟩