"Resumen de Tesis Doctoral" Fast Most Similar Neighbor (MSN) classifiers for Mixed Data Clasificadores Rápidos basados en el algoritmo del Vecino más Similar (MSN) para Datos Mezclados
作者： Jesús Ariel , Carrasco Ochoa , Selene Hernández Rodríguez , Santa María Tonantzintla , Luis Enrique Erro
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摘要: The k nearest neighbor (k-NN) classifier has been extensively used in Pattern Recognition because of its simplicity and good performance. However, large datasets applications, the exhaustive k-NN becomes impractical. Therefore, many fast classifiers have developed; most them rely on metric properties (usually triangle inequality) to reduce number prototype comparisons. Hence, existing are applicable only when comparison function is a (commonly for numerical data). some sciences such as Medicine, Geology, Sociology, etc., prototypes usually described by qualitative quantitative features (mixed In these cases, does not necessarily satisfy properties. For this reason, it important develop similar (k-MSN) mixed data, which use non comparisons functions. thesis, four k-MSN classifiers, following successful approaches, proposed. experiments over different show that proposed significantly
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