Improving the k-nearest neighbor classifier using the Cheetah optimization algorithm to increase classification performance

Feature selection has emerged as a combinatorial optimization problem where the focus is on selecting a subset of input features that efficiently represent the input data, reducing the effects of noisy and irrelevant variables, and providing acceptable predictive outcomes. Various methods, including filter, wrapper, and embedded approaches, exist for feature selection. Nowadays, meta-heuristic search algorithms, particularly those under the wrapper method, are widely used in feature selection problems. In recent years, inspired by nature, various evolutionary algorithms have been proposed for the feature selection problem, indicating its high significance. All of these algorithms are attempting to increase classification accuracy by reducing the number of features. Therefore, in this research, the Cheetah Optimization Algorithm (CHOA) is employed to enhance the speed and accuracy of k-nearest neighbor classifier. Reference datasets are used to evaluate the results of the proposed method. The evaluation results demonstrate that the proposed approach outperforms other methods in terms of classification accuracy.