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IDD: a supervised interval distance-based method for discretization

Autor
Ruiz, F.; Angulo, C.; Agell, N.
Tipus d'activitat
Article en revista
Revista
IEEE transactions on knowledge and data engineering
Data de publicació
2008-09
Volum
20
Número
9
Pàgina inicial
1230
Pàgina final
1238
DOI
https://doi.org/10.1109/TKDE.2008.66 Obrir en finestra nova
Projecte finançador
EXODUS. Red de sensores corporales basada en elementos hardware inteligentes para una experiencia optimizada de usuario
Sistemas de aprendizaje automático con razonamiento cualitativo
Repositori
http://sci2s.ugr.es/keel/pdf/algorithm/articulo/2008-Ruiz-IEEETKDE.pdf Obrir en finestra nova
URL
http://ieeexplore.ieee.org/abstract/document/4492776/ Obrir en finestra nova
Resum
This article introduces a new method for supervised discretization based on interval distances by using a novel concept of neighbourhood in the target's space. The method proposed takes into consideration the order of the class attribute, when this exists, so that it can be used with ordinal discrete classes as well as continuous classes, in the case of regression problems. The method has proved to be very efficient in terms of accuracy and faster than the most commonly supervised discretization...
Paraules clau
Classification, Clustering, Interval arithmetic, Interval distances, Mining methods and algorithms, Ordinal regression, Supervised discretization, and association rules, classification
Grup de recerca
GREC - Grup de Recerca en Enginyeria del Coneixement
IDEAI-UPC Intelligent Data Science and Artificial Intelligence

Participants