Enhanced Distance-based Weighted K-Nearest Neighbor Algorithm for Data Classification

The k-Nearest Neighbors (kNN) algorithm is one of the most widely used techniques for data classification.However, the imbalanced class is a key problem for its declining performance.Therefore, the kNN algorithm is kept updated to mitigate this problem's effects.Following the pattern of the literature, our paper proposes a novel weighted kNN that aims to significantly reduce the negative consequences of this problem.An enhanced distance-based weighted KNN, EDWkNN, is developed to improve the overall KNN performance.For the test sample, the number of neighbors (k) is initially determined.Next, normalized weights are computed for these neighbors and assigned a value between 0 and 1.The weights of the nearest and farthest neighbors are 1 and 0, respectively.The weight values of the remaining neighbors range from 0 to 1, with the closest one having a heavier weight and the farthest neighbor having a lower weight value.In extreme circumstances, a few neighbors have equal distance from the test sample, leading to assigning uniform weights to each of these neighbors.Further, the EDWkNN considers both the magnitude of the distance and the proximity of neighbors.Comparing EDWkNN to its state-of-the-art rivals, its simplistic architecture and competitive performance demonstrate its uniqueness.In four experimental phases, a thorough assessment study is conducted utilizing four evaluation metrics (accuracy, precision, recall, and MAE) over forty-four datasets.The findings show that, on average and for individual k values, EDWkNN is substantially promising.