An evolutionary voting for k-nearest neighbours

• We optimize the voting system for the k nearest neighbours.
• We use evolutionary computation.
• We study the influence of the closeness of neighbours on the search process.
• The results are statistically validated.

This work presents an evolutionary approach to modify the voting system of the k-nearest neighbours (kNN) rule we called EvoNN. Our approach results in a real-valued vector which provides the optimal relative contribution of the k-nearest neighbours. We compare two possible versions of our algorithm. One of them (EvoNN1) introduces a constraint on the resulted real-valued vector where the greater value is assigned to the nearest neighbour. The second version (EvoNN2) does not include any particular constraint on the order of the weights. We compare both versions with classical kNN and 4 other weighted variants of the kNN on 48 datasets of the UCI repository. Results show that EvoNN1 outperforms EvoNN2 and statistically obtains better results than the rest of the compared methods.