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

Blockeel, Hendrik; Bogaerts, Bart; Bruynooghe, Maurice; De Cat, Broes; De Pooter, Stef; Denecker, Marc; Labarre, Anthony; Ramon, Jan; Verwer, Sicco

doi:10.4230/LIPIcs.ICLP.2012.14

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.

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

Authors Hendrik Blockeel, Bart Bogaerts, Maurice Bruynooghe, Broes De Cat, Stef De Pooter, Marc Denecker, Anthony Labarre, Jan Ramon, Sicco Verwer

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