A Cooperative Coevolutionary Algorithm For KNN Training Set Optimization

The traditional evolutionary instance selection algorithm has the risk of redundant and noise training samples in the training set selection, which affects the classification effect. In this paper, instance selection, instance weighting and feature weighting are integrated into the cooperative coevolution framework, and a cooperative coevolutionary algorithm for KNN training set optimization selection is proposed. The CHC algorithm based on multi-point crossover strategy is used to further improve the accuracy of instance selection. The SSGA algorithm based on fast mutation strategy of instance weighting and feature weighting is synergistic with the instance selection, and it helps to remove noise and irrelevant data in the process of instance selection. The proposed method can speed up the convergence of the population, it can also improve the efficiency of the algorithm, and improve the KNN classification performance. The experimental results show that this method has advantages in classification accuracy and efficiency compared with some current evolutionary instance selection algorithms.