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On the evolutionary optimization of k-NN by label-dependent feature weighting

 

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Opened Access On the evolutionary optimization of k-NN by label-dependent feature weighting
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Author: Mateos García, Daniel
García Gutiérrez, Jorge
Riquelme Santos, José Cristóbal
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2012
Published in: Pattern Recognition Letters, 33 (16), 2232-2238.
Document type: Article
Abstract: 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.
Cite: 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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URI: http://hdl.handle.net/11441/43441

DOI: 10.1016/j.patrec.2012.08.011

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