Selecting of the best features for the knn classification method by Harris Hawk algorithm

Today, due to the difficulty and complexity of selecting important and basic features from theinitial data, and also classifying the relevant features in terms of accuracy and training time,many methods in this field have been proposed by researchers that aim to All of them havebeen to simplify the selection of features and also increase the classification accuracy of therelevant features. Because performing the feature selection process improves classificationperformance and reduces training time and computational complexity. Therefore, in thispaper, the Harris Hawks Optimization (HHO) algorithm is described in order to extract themain features from the relevant data and remove duplicate features from the data, which fromthe k-nearest neighbor (knn) classification method is used as a fitness function to measure theaccuracy of classification.