Curvas ROC y Vecinos Cercanos, Propuesta de un Nuevo Algoritmo de Condensación

k-NN criteria are non parametric methods of statistical classificaction. They are accurate, versatile and distribution free. However,  their computational cost may be too expensive; especially for large sample sizes. We present a new condensation algorithm based on the Binormal model for ROC curves....

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Autores Principales: Jiménez Padilla, Raquel, Cuevas Covarrubias, Carlos
Formato: Artículo
Idioma: Español
Publicado: 2015
Acceso en línea: http://revistas.ucr.ac.cr/index.php/matematica/article/view/2112
http://hdl.handle.net/10669/12983
Sumario: k-NN criteria are non parametric methods of statistical classificaction. They are accurate, versatile and distribution free. However,  their computational cost may be too expensive; especially for large sample sizes. We present a new condensation algorithm based on the Binormal model for ROC curves. It transforms the training sample into a small set of low dimensional vetors. Contrasting with other condensation techniques described in the literature, our proposal helps to control the exchange of accuracy for condensation onthe training sample. The results of a Monte Carlo study show that its performance can be very competitive in different realistic scenarios, resulting in better training samples than other frequently used methods.