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

By Daniel Mateos García, Jorge García Gutiérrez and José Cristóbal Riquelme Santos


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

Topics: Feature weighting, evolutionary computation, Label dependency
Publisher: 'Elsevier BV'
Year: 2012
DOI identifier: 10.1016/j.patrec.2012.08.011
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