Artículo
An evolutionary voting for k-nearest neighbours
Autor/es | Mateos García, Daniel
García Gutiérrez, Jorge Riquelme Santos, José Cristóbal |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2016 |
Fecha de depósito | 2016-07-15 |
Publicado en |
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Resumen | This work presents an evolutionary approach to modify the voting system of the k-nearest neighbours (kNN) rule we called EvoNN. Our approach results in a real-valued vector which provides the optimal relative con-tribution ... This work presents an evolutionary approach to modify the voting system of the k-nearest neighbours (kNN) rule we called EvoNN. Our approach results in a real-valued vector which provides the optimal relative con-tribution of the k-nearest neighbours. We compare two possible versions of our algorithm. One of them (EvoNN1) introduces a constraint on the resulted real-valued vector where the greater value is assigned to the nearest neighbour. The second version (EvoNN2) does not include any particular constraint on the order of the weights. We compare both versions with classical kNN and 4 other weighted variants of the kNN on 48 datasets of the UCI repository. Results show that EvoNN1 outperforms EvoNN2 and statistically obtains better results than the rest of the compared methods. |
Cita | Mateos García, D., García Gutiérrez, J. y Riquelme Santos, J.C. (2016). An evolutionary voting for k-nearest neighbours. Expert Systems with Applications, 43, 9-14. |
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