New Neighborhood Based Classification Rules for Metric Spaces and Their Use in Ensemble Classification

Authors:
Jose-Norberto Mazón;Luisa Micó;Francisco Moreno-Seco
Affiliations:
Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, P.O. box 99, E-03080 Alicante, Spain;Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, P.O. box 99, E-03080 Alicante, Spain;Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, P.O. box 99, E-03080 Alicante, Spain
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

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Abstract

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.