Fast k most similar neighbor classifier for mixed data based on a tree structure

Authors:
Selene Hernández-Rodríguez;J. Fco. Martínez-Trinidad;J. Ariel Carrasco-Ochoa
Affiliations:
Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, México;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, México;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla, México
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

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Abstract

In this work, a fast k most similar neighbor (k-MSN) classifier for mixed data is presented. The k nearest neighbor (k-NN) classifier has been a widely used nonparametric technique in Pattern Recognition. Many fast k-NN classifiers have been developed to be applied on numerical object descriptions, most of them based on metric properties to avoid object comparisons. However, in some sciences as Medicine, Geology, Sociology, etc., objects are usually described by numerical and non numerical features (mixed data). In this case, we can not assume the comparison function satisfies metric properties. Therefore, our classifier is based on search algorithms suitable for mixed data and nonmetric comparison functions. Some experiments and a comparison against other two fast k-NN methods, using standard databases, are presented.