Analogical Classification: A Rule-Based View - Archive ouverte HAL Access content directly
Conference Papers Year : 2014

Analogical Classification: A Rule-Based View


Analogical proportion-based classification methods have been introduced a few years ago. They look in the training set for suitable triples of examples that are in an analogical proportion with the item to be classified, on a maximal set of attributes. This can be viewed as a lazy classification technique since, like k-nn algorithms, there is no static model built from the set of examples. The amazing results (at least in terms of accuracy) that have been obtained from such techniques are not easy to justify from a theoretical viewpoint. In this paper, we show that there exists an alternative method to build analogical proportion-based learners by statically building a set of inference rules during a preliminary training step. This gives birth to a new classification algorithm that deals with pairs rather than with triples of examples. Experiments on classical benchmarks of the UC Irvine repository are reported, showing that we get comparable results.
Fichier principal
Vignette du fichier
bouhnas_15181.pdf (268.85 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01394666 , version 1 (09-11-2016)


  • HAL Id : hal-01394666 , version 1
  • OATAO : 15181


Myriam Bounhas, Henri Prade, Gilles Richard. Analogical Classification: A Rule-Based View. 15th International Conference on Information Processing and Management (IPMU 2014), Jul 2014, Montpellier, France. pp. 485-495. ⟨hal-01394666⟩
90 View
108 Download


Gmail Facebook X LinkedIn More