Abstract: The K-Nearest Neighbors (KNN) algorithm is a classical supervised learning method widely used in classification and regression ... In this study, an optimization strategy based on balanced ...

Loading dataset, call CSA for parameter optimization and classification by deep neural network are the most important commands that there are in the main file. CSA.m: The edited version of crow search ...

Abstract: K-nearest neighbor (K-NN) algorithm is a classification method based on statistical theory. In this algorithm the Euclidean distance is usually chosen as the similarity measure, which ...

With the help of real-valued EA algorithms, direct optimization of the spatial filter with respect to the classification accuracy can be performed where the convexity may not be preserved in the cost ...

Methods: The INGHS algorithm is applied to ... frequency-temporal parameters in the optimization process so that the CSP features obtained by the optimal frequency-time parameters can enhance the ...