A novel k-NN approach for data with uncertain attribute values - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

A novel k-NN approach for data with uncertain attribute values

Résumé

Data uncertainty arises in several real world domains, including machine learning and pattern recognition applications. In classification problems, we could very well wind up with uncertain attribute values that are caused by sensor failures, measurements approximations or even subjective expert assessments, etc. Despite their seriousness, these kinds of data are not well covered till now. In this paper, we propose to develop a machine learning model for handling such kinds of imperfection. More precisely, we suggest to develop a new version of the well known k-nearest neighbors classifier to handle the uncertainty that occurs in the attribute values within the belief function framework.
Fichier principal
Vignette du fichier
iea-iae.pdf (286.65 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03649432 , version 1 (22-04-2022)

Identifiants

Citer

Asma Trabelsi, Zied Elouedi, Eric Lefevre. A novel k-NN approach for data with uncertain attribute values. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE'2017, Jun 2017, Arras, France. pp.160-170, ⟨10.1007/978-3-319-60042-0_19⟩. ⟨hal-03649432⟩

Collections

UNIV-ARTOIS LGI2A
11 Consultations
17 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More