Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [17 references]  Display  Hide  Download
Contributor : Anthony Labarre Connect in order to contact the contributor
Submitted on : Wednesday, September 12, 2012 - 5:42:08 PM
Last modification on : Tuesday, October 19, 2021 - 12:55:33 PM
Long-term archiving on: : Friday, December 16, 2016 - 12:50:50 PM


Publisher files allowed on an open archive


  • HAL Id : hal-00731459, version 1



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⟩



Record views


Files downloads