Fast Most Similar Neighbor Classifier for Mixed Data

Authors:
Selene Hernández-Rodríguez;J. Francisco Martínez-Trinidad;J. Ariel Carrasco-Ochoa
Affiliations:
Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840, Mexico;Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840, Mexico
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

The nearest neighbor (NN) classifier has been a widely used technique in pattern recognition because of its simplicity and good behavior. To decide the class of a new object, the NNclassifier performs an exhaustive comparison between the object to classify and the training set T. However, when Tis large, the exhaustive comparison is very expensive and sometimes becomes inapplicable. To avoid this problem, many fast NNalgorithms have been developed for numerical object descriptions, most of them based on metric properties to avoid comparisons. However, in some sciences as Medicine, Geology, Sociology, etc., objects are usually described by numerical and non numerical attributes (mixed data). In this case, we can not assume the comparison function satisfies metric properties. Therefore, in this paper a fast most similar object classifier based on search methods suitable for mixed data is presented. Some experiments using standard databases and a comparison with other two fast NNmethods are presented.