Analysis of the Nearest Neighbor Classifiers: A Review

Yash Agarwal, G. Poornalatha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


We are living in a data age and with the expansion of ‘Internet of Things’ platform, there is an upsurge in devices connected to the Internet. Everything from smart sensors to smartphones and tablets, systems installed in manufacturing units, hospitals, vehicles, etc. is generating data. Such developments in the technological world have escalated the generation of data and require an analysis to be performed on the raw data to identify patterns. The data mining techniques are deployed extensively to extract information and they yield far-reaching effects on the trade and the lives of the people concerned. The accuracy and effectiveness of data mining techniques in providing better outcomes and cost-effective methods in various domains have been established. Usually, in supervised learning, density estimation is used by instance-based learning classifiers like k-nearest neighbor (kNN). In this paper, the regular kNN classifier is compared with the various classifiers conceptually and the ARSkNN that uses mass estimation has been proved to be commensurate to kNN in accuracy and has reduced computation time drastically on datasets chosen for this analysis. Tenfold cross-validation is used for testing.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence and Data Engineering - Select Proceedings of AIDE 2019
EditorsNiranjan N. Chiplunkar, Takanori Fukao
PublisherSpringer Gabler
Number of pages12
ISBN (Print)9789811535130
Publication statusPublished - 2021
EventInternational Conference on Artificial Intelligence and Data Engineering, AIDE 2019 - Mangalore, India
Duration: 23-05-201924-05-2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Artificial Intelligence and Data Engineering, AIDE 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • General Computer Science


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