A Brief Review of Nearest Neighbor Algorithm for Learning and Classification

By Kashvi Taunk, Sá De, S. Verma, A. Swetapadma. International Conference on Conceptual Structures, 2019

676 citations recorded by the source corpus.

k-Nearest Neighbor (kNN) algorithm is an effortless but productive machine learning algorithm. It is effective for classification as well as regression. However, it is more widely used for classification prediction. kNN groups the data into coherent clusters or subsets and classifies the newly inputted data based on its similarity with previously trained data. The input is assigned to the class with which it shares the most nearest neighbors. Though kNN is effective, it has many weaknesses. This paper highlights the kNN method and its modified versions available in previously done researches. These variants remove the weaknesses of kNN and provide a more efficient method.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bcfb398e5bd1a63ebb35ebab212ca9b07ff654be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Joaquim R. Martins
2. Ji-chao Li
3. Xiaosong Du
4. Gaetan K. W. Kenway

In Computer Vision and Pattern Recognition

1. Joaquim R. Martins
2. Ji-chao Li
3. Xiaosong Du

Citations in the corpus

• Machine Learning in Aerodynamic Shape Optimization (2022, background): “On the one hand, KNN is simple to implement, flexible to classification and regression problems, and does well with sufficient representative data.”