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

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.

► A novel evolutionary approach called kLDEDW is proposed. ► kLDEDW locally transforms the feature space to improve the accuracy of the k-NN. ► We show the efficiency of a local feature weighting method with statistical tests.