PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

IDD: A Supervised Interval Distance-Based Method for Discretization
Francisco Javier Ruiz, Cecilio Angulo and Núria Agell
IEEE Transactions on Knowledge and Data Engineering Volume 20, Number 9, pp. 1230-1238, 2008. ISSN 1041-4347

Abstract

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 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4527
Deposited By:Cecilio Angulo
Deposited On:13 March 2009