Feature Selection From Partially Uncertain Data Within the Belief Function Framework - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2016

Feature Selection From Partially Uncertain Data Within the Belief Function Framework

Résumé

With the rapid growth of high dimensional data, feature selection has become a substantial task for several machine learning problems. In fact, it is regarded as an important process for classification performance owing to its ability to remove redundant and inconsistent features. The rough set theory is regarded as a well known tool allowing relevant feature selection. As the task of attribute selection using rough sets is an NP-hard problem, several heuristic algorithms have been introduced. The Johnson's algorithm, handling data characterized by certain and precise attribute values, is one of the most known ones. In this paper, we propose to extend this latter algorithm to an uncertain context, precisely where data contain uncertain condition attribute values represented within the belief function framework. We test the performance of our belief Johnson's algorithm through several experiments on synthetic databases.
Fichier principal
Vignette du fichier
IPMU_Finale.pdf (279.81 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03649461 , version 1 (22-04-2022)

Identifiants

Citer

Asma Trabelsi, Zied Elouedi, Eric Lefevre. Feature Selection From Partially Uncertain Data Within the Belief Function Framework. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPUM'2016, Jun 2016, Eindhoven, Netherlands. pp.643-655, ⟨10.1007/978-3-319-40581-0_52⟩. ⟨hal-03649461⟩

Collections

UNIV-ARTOIS LGI2A
10 Consultations
18 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More