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


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

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Author: García Gutiérrez, Jorge
Mateos García, Daniel
Riquelme Santos, José Cristóbal
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2014
Published in: 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
Document type: Chapter of Book
Abstract: 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.
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