PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

New neighborhood based classification rules for metric spaces and their use in ensemble classification
Jose Norberto Mazon, Luisa Mico and Francisco Moreno-Seco
In: Third Iberian Conference, IbPRIA 2007, 6-8 June 2007, Girona, Spain.


The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification tasks. Based on the neighborhood concept, several classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification rules derived from them are used in several real data classification tasks. Also, some voting ensembles of classifiers based on these new rules have been tested and compared. \

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3374
Deposited By:Luisa Mico
Deposited On:09 February 2008