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Non-parametric Nearest Neighbor with Local Adaptation

Opened Access Non-parametric Nearest Neighbor with Local Adaptation


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Autor: Ferrer Troyano, Francisco J.
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2001
Publicado en: Progress in Artificial Intelligence, Notes in Computer Science, Volume 2258, pp 22-29 (2001)
Tipo de documento: Capítulo de Libro
Resumen: The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value of this parameter must be determined by the user. In this paper we present an algorithm based on the NN technique that does not take the value of k from the user. Our approach evaluates values of k that classified the training examples correctly and takes which classified most examples. As the user does not take part in the election of the parameter k, the algorithm is non-parametric. With this heuristic, we propose an easy variation of the k-NN algorithm that gives robustness with noise present in data. Summarized in the last section, the experiments show that the error rate decreases in comparison with the k-NN technique when the best k for each database has been previously obtained.
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