An improved KNN algorithm based on ensemble methods and correlation

K-Nearest Neighbors is a widely used algorithm due to its simplicity and efficacity. However, KNN suffers from many drawbacks, such as it does not work well with datasets with a high number of features. Also, not all the features contribute to the classification process. To resolve these issues, we present an improved KNN algorithm which uses KNN as a base learner in an ensemble method and correlation for selecting the features subsets. The experiment results show that the proposed algorithm performed better than other machine learning algorithms.