Weighted K-nearest neighbors classification based on Whale optimization algorithm

K-Nearest Neighbors (KNN) is a classification algorithm based on supervised machine learning, which works according to a voting system. The performance of the KNN algorithm depends on different factors, such as unbalanced distribution of classes, the scalability problem, and considering equal values for all training samples. Regarding the importance of the KNN algorithm, different improved versions of this algorithm are introduced, such as fuzzy KNN, weighted KNN, and KNN with variable neighbors. In this paper, a weighted KNN based on Whale Optimization Algorithm is proposed for the objective of increasing the level of detection accuracy. The proposed algorithm devotes a weight to each training sample of every feature by employing the WOA to explore the optimized weight matrix. The algorithm is implemented and experimented on five standard datasets. The evaluation results prove that the proposed algorithm performs better than both weighted KNN based on the Genetic Algorithm (GA) and the classic KNN algorithm.

کلیدواژه ها:

K-nearest neighbors ، weighted K-nearest neighbors ، Whale optimization algorithm ، Genetic Algorithm