Applications of K Nearest Neighbor algorithm part1(Artificial Intelligence)

Abstract: Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The k-Nearest Neighbors is one of the most effective and straightforward models employed in numerous problems. Despite its well-known performance, it requires the value of k for specific data distribution, thus demanding expensive computational efforts. This paper proposes a k-Nearest Neighbors classifier that bypasses the need to define the value of k. The model computes the k value adaptively considering the data distribution of the training set.