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TKDE
2008

IDD: A Supervised Interval Distance-Based Method for Discretization

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IDD: A Supervised Interval Distance-Based Method for Discretization
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.
Francisco J. Ruiz, Cecilio Angulo, Núria Ag
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TKDE
Authors Francisco J. Ruiz, Cecilio Angulo, Núria Agell
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