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

Authors:
Daniel Mateos-GarcíA;Jorge GarcíA-GutiéRrez;José C. Riquelme-Santos
Affiliations:
Department of Computer Science, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain;Department of Computer Science, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain;Department of Computer Science, University of Seville, Avda. Reina Mercedes s/n, 41012 Seville, Spain
Venue:
Pattern Recognition Letters
Year:
2012

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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.