Artículo
On the evolutionary optimization of k-NN by label-dependent feature weighting
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 | 2012 |
Fecha de depósito | 2016-07-11 |
Publicado en |
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Resumen | Different approaches of feature weighting and k-value selection to improve the nearest neighbour technique
can be found in the literature. In this work, we show an evolutionary approach called k-Label
Dependent Evolutionary ... Different approaches of feature weighting and k-value selection to improve the nearest neighbour technique can be found in the literature. In this work, we show an evolutionary approach called k-Label Dependent Evolutionary Distance Weighting (kLDEDW) which calculates a set of local weights depending on each class besides an optimal k value. Thus, we attempt to carry out two improvements simultaneously: we locally transform the feature space to improve the accuracy of the k-nearest-neighbour rule whilst we search for the best value for k from the training data. Rigorous statistical tests demonstrate that our approach improves the general k-nearest-neighbour rule and several approaches based on local weighting. |
Cita | Mateos García, D., García Gutiérrez, J. y Riquelme Santos, J.C. (2012). On the evolutionary optimization of k-NN by label-dependent feature weighting. Pattern Recognition Letters, 33 (16), 2232-2238. |
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