New neighborhood based classification rules for metric spaces and their use in ensemble classification

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.This work has been supported in part by grant DPI2006-15542-C04-01 from the Spanish CICYT (Ministerio de Ciencia y Tecnología), GV06/166 from Generalitat Valenciana, and the IST Programme of the European Community, under the Pascal Network of Excellence, IST-2002-506778