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Improving the k-Nearest Neighbour Rule by an Evolutionary Voting Approach

Opened Access Improving the k-Nearest Neighbour Rule by an Evolutionary Voting Approach


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Autor: García Gutiérrez, Jorge
Mateos García, Daniel
Riquelme Santos, José Cristóbal
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2014
Publicado en: Hybrid Artificial Intelligence Systems: 9th International Conference, HAIS 2014, Salamanca, Spain, June 11-13, 2014. Proceedings. Lecture Notes in Computer Science, v.8480
ISBN/ISSN: 978-3-319-07617-1
Tipo de documento: Capítulo de Libro
Resumen: This work presents an evolutionary approach to modify the voting system of the k-Nearest Neighbours (kNN). The main novelty of this article lies on the optimization process of voting regardless of the distance of every neighbour. The calculated real-valued vector through the evolutionary process can be seen as the relative contribution of every neighbour to select the label of an unclassified example. We have tested our approach on 30 datasets of the UCI repository and results have been compared with those obtained from other 6 variants of the kNN predictor, resulting in a realistic improvement statistically supported.
Tamaño: 221.4Kb
Formato: PDF



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