IDD: A supervised interval distance-based method for discretization

Francisco J. Ruiz, Cecilio Angulo, Núria Agell

Producció científica: Article en revista indexadaArticleAvaluat per experts

31 Cites (Scopus)


This paper introduces a new method for supervised discretization based on interval distances by using a novel concept of neighborhood in the target's space. The proposed method 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 methods used in the literature. It is illustrated through several examples, and a comparison with other standard discretization methods is performed for three public data sets by using two different learning tasks: a decision tree algorithm and SVM for regression.

Idioma originalAnglès
Número d’article4492776
Pàgines (de-a)1230-1238
Nombre de pàgines9
RevistaIEEE Transactions on Knowledge and Data Engineering
Estat de la publicacióPublicada - de set. 2008
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