Research Article | OPEN ACCESS
Extensions of k-Nearest Neighbor Algorithm
Rashmi Agrawal
Faculty of Computer Applications, Manav Rachna International University, Faridabad,
Haryana 121004, India
Research Journal of Applied Sciences, Engineering and Technology 2016 1:24-29
Received: November 17, 2015 | Accepted: February 10, 2016 | Published: July 05, 2016
Abstract
The aim of this study is to review various extensions of the nearest neighbor algorithm and discuss their approach along with limitations of the method. In nonparametric classification, no prior information is required for predicting the class label. k-Nearest Neighbor is the simplest and well known algorithm used in data mining The various extensions of k-nearest neighbor algorithm which have been studied are weighted nearest neighbor, feature selection methods, fuzzy nearest neighbor, genetic algorithm based classifiers and nearest neighbor algorithm using ensembling techniques.
Keywords:
Ensembling, feature selection, fuzzy set, genetic algorithm , nonparametric classification , weighted nearest neighbor,
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Competing interests
The authors have no competing interests.
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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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