Communication Dans Un Congrès Année : 1997

Distance induction in first order logic

Résumé

A distance on the problem domain allows one to tackle some typical goals of machine learning, e.g. classification or conceptual clustering, via robust data analysis algorithms (e.g. k-nearest neighbors or k-means). A method for building a distance on first-order logic domains is presented in this paper. The distance is constructed from examples expressed as definite or constrained clauses, via a two-step process: a set of d hypotheses is :first learnt from the training examples. These hypotheses serve as new descriptors of the problem domain Lh: they induce a mapping π from Lh onto the space of integers Nd. The distance between any two examples E and F is finally defined as the Euclidean distance between π(E) and π(F). The granularity of this hypothesis-driven distance (HDD) is controlled via the user-supplied parameter d. The relevance of a HDD is evaluated from the predictive accuracy of the k-NN classifier based on this distance. Preliminary experiments demonstrate the potentialities of distance induction, in terms of predictive accuracy, computational cost, and tolerance to noise.

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relationship_isVariantFormatOf hal-00117890 Objet présenté à une conférence Michèle Sebag. DISTILL: Induction de distance en logique du premier ordre. 13es Journées Francophones de l'Apprentissage (JFA), 1998, Paris, France. ⟨hal-00117890⟩

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hal-00116475 , version 1 (20-08-2021)

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Michèle Sebag. Distance induction in first order logic. International Conference on Inductive Logic Programming (ILP97), 1997, Prague, Czech Republic. pp.264-272, ⟨10.1007/3540635149_55⟩. ⟨hal-00116475⟩
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