Assessing Performance and Crossover Operators on Differential Evolution for Attribute Weighting

Abstract

Machine learning has a wide range of applications, including classification, which categorizes elements based on their characteristics. This paper addresses the challenge of optimizing attribute weighting while assessing two crossover operators on differential evolution optimization and increasing the performance of the k-nearest neighbors classification algorithm (KNN). We use a differential evolution optimization method and assess the performance of both the differential evolution and the harmony crossover operators. Finally, the optimization method uses the accuracy of a KNN classification algorithm as a fitness function. The results show that the proposed method significantly enhances the KNN performance while proposing an alternative for other classification models such as neural networks and Random Forest.