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 ...
This work presents an evolutionary approach to modify the voting system of the k-Nearest Neighbours (kNN). The main novelty of this article lies on the ...
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 ...
The proposed method is called evolutionary optimized feature and class weights kNN (FCWkNN). The performance of FCWkNN is examined on different datasets.
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- ...
On the evolutionary optimization of k-NN by label-dependent feature weighting · Improving the k-Nearest Neighbour Rule by an Evolutionary Voting Approach.
Rigorous statistical tests demonstrate that our approach improves the general k-nearest-neighbour rule and several approaches based on local weighting.
In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951.
This study proposes using genetic algorithm (GA) to find an appropriate distance function and a class-voting mechanism for kNN on imbalanced datasets. GA is a ...
In this work we present a new co-evolutionary algorithm for combining the former techniques. Its performance is compared with evolutionary approaches performing ...