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.

Automated summary

K-Nearest Neighbors (KNN) is a classification algorithm that uses supervised machine learning and a voting system. The performance of KNN depends on factors like class distribution, scalability, and equal values for all training samples. Variations of KNN, such as fuzzy KNN, weighted KNN, and KNN with variable neighbors, have been proposed to improve its accuracy. This paper introduces a weighted KNN based on the Whale Optimization Algorithm, which assigns weights to training samples using an optimized weight matrix. Experimental results show that the proposed algorithm outperforms both weighted KNN based on Genetic Algorithm (GA) and classic KNN.