Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

A Non-parametric Semi-supervised Discretization Method

Abstract :

Semi-supervised classification methods aim to exploit labelled and unlabelled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method which adopts very low informative prior on data. This method discretizes the numerical domain of a continuous input variable, while keeping the information relative to the prediction of classes. Then, an in-depth comparison of this semi-supervised method with the original supervised MODL approach is presented. We demonstrate that the semi-supervised approach is asymptotically equivalent to the supervised approach, improved with a post-optimization of the intervals bounds location.

Type de document :
Communication dans un congrès
Liste complète des métadonnées

https://hal.univ-angers.fr/hal-03255388
Contributeur : Okina Université d'Angers Connectez-vous pour contacter le contributeur
Soumis le : mercredi 9 juin 2021 - 15:04:16
Dernière modification le : jeudi 10 juin 2021 - 03:39:57

Identifiants

Collections

Citation

A Bondu, M. Boulle, V. Lemaire, Stéphane Loiseau, Béatrice Duval. A Non-parametric Semi-supervised Discretization Method. Eighth IEEE International Conference on Data Mining, 2008. ICDM '08, 2008, Pise, Italy. pp.53 - 62, ⟨10.1109/ICDM.2008.35⟩. ⟨hal-03255388⟩

Partager

Métriques

Consultations de la notice

12