- -

Extraction of decision rules via imprecise probabilities

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Extraction of decision rules via imprecise probabilities

Show full item record

Abellán, J.; López-Maldonado, G.; Garach, L.; Castellano, JG. (2017). Extraction of decision rules via imprecise probabilities. International Journal of General Systems. 46(4):313-331. https://doi.org/10.1080/03081079.2017.1312359

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/149156

Files in this item

Item Metadata

Title: Extraction of decision rules via imprecise probabilities
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería e Infraestructura de los Transportes - Departament d'Enginyeria i Infraestructura dels Transports
Issued date:
Abstract:
Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via ...[+]
Subjects: Imprecise probabilities , Imprecise Dirichlet model , Non-parametric predictive inference model , Uncertainty measures , Decision rules , Traffic accident severity
Copyrigths: Reserva de todos los derechos
Source:
International Journal of General Systems. (issn: 0308-1079 )
DOI: 10.1080/03081079.2017.1312359
Publisher:
Taylor & Francis
Publisher version: https://doi.org/10.1080/03081079.2017.1312359
Description: "This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of General Systems on 2017, available online: https://www.tandfonline.com/doi/full/10.1080/03081079.2017.1312359"
Thanks:
This work has been supported by the Spanish "Ministerio de Economia y Competitividad" [Project number TEC2015-69496-R] and FEDER funds.
Type: Artículo

This item appears in the following Collection(s)

Show full item record