Challenges in KNN Classification

The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 34; no. 10; pp. 4663 - 4675
Main Author: Zhang, Shichao
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Classification
classification rule
Data analysis
Data mining
KNN classification
lazy learning
Licenses
Nearest neighbor methods
Partitioning algorithms
Prediction algorithms
Training
Training data
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN classification, including K computation, nearest neighbor selection, nearest neighbor search and classification rules. Having established these issues, recent approaches to their resolution are examined in more detail, thereby providing a potential roadmap for ongoing KNN-related research, as well as some new classification rules regarding how to tackle the issue of training sample imbalance. To evaluate the proposed approaches, some experiments were conducted with 15 UCI benchmark datasets.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2021.3049250