PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On the use of different classification rules in an editing task.
Luisa Mico, F. Moreno-Seco, J.S. Sánchez, J.M. Sotoca and R.A. Mollineda
In: Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2006) and Statistical Techniques in Pattern Recognition (SPR 2006), 17-19 Aug 2006, Hong Kong.


Editing allows the selection of a representative subset of prototypes among the training sample to improve the performance of a classification task. The Wilson's editing algorithm was the first proposal and then a great variety of new editing techniques have been proposed based on it. This algorithm consists on the elimination of prototypes in the training set that are misclassified using the k-NN rule. From such editing scheme, a general editing procedure can be straightforward derived, where any classifier beyond k-NN can be used. In this paper, we analyze the behavior of this general editing procedure combined with 3 different neighborhood-based classification rules, including k-NN. The results reveal better performances of the 2 other techniques with respect to k-NN in most of cases.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2527
Deposited By:Luisa Mico
Deposited On:22 November 2006