A parameter-free nearest neighbor algorithm with reduced prediction time and improved performance through injected randomness

K-nearest neighbor is considered in top machine learning algorithms because of its effectiveness in pattern classification and simple implementation. However, usage of KNN is limited due to its larger prediction time than model-based machine learning algorithms, its sensitivity to the existing outli...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 37; no. 17; pp. 10531 - 10556
Main Authors:	Singh, Manpreet, Chhabra, Jitender Kumar
Format:	Journal Article
Language:	English
Published:	London Springer London 01.06.2025 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Image Processing and Computer Vision Machine learning Parameter sensitivity Pattern classification Probability and Statistics in Computer Science Randomness S.I.: Timely Advances of Deep Learning with applications and Data Driven Modeling Special Issue on Timely Advances of Deep Learning with applications and Data Driven Modeling Binary search tree Nearest neighbors K-means clustering Injected randomness
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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