Supervised Classification Using Homogeneous Logical Proportions for Binary and Nominal Features

Ronei Moraes (1, 2) , Liliane Machado (1, 2) , Henri Prade (3, 4) , Gilles Richard (3)

1 Laboratório de Tecnologias para o Ensino Virtual e Estatística - LabTEVE (Joao Pessoa, Brazil)
2 IRIT - Institut de recherche en informatique de Toulouse
3 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
4 CNRS - Centre National de la Recherche Scientifique

The notion of homogeneous logical proportions has been recently introduced in close relation with the idea of analogical proportion. The four homogeneous proportions have intuitive meanings, which can be related with classification tasks. In this paper, we proposed a supervised classification algorithm using homogeneous logical proportions and provide results for all. A final comparison with previous works using similar methodologies and with other classifiers is provided.

Mots clés: Analogical proportion; Analogical dissimilarity; Supervised classification

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Logique en informatique [cs.LO]; Informatique et langage [cs.CL]

Ronei Moraes, Liliane Machado, Henri Prade, Gilles Richard. Supervised Classification Using Homogeneous Logical Proportions for Binary and Nominal Features. 18th Iberoamerican Congress on Pattern Recognition (CIARP 2013), Nov 2013, Havana, Cuba. pp. 165-173. ⟨hal-01197280⟩