Balanced-kNN: A New Lazy Learning Algorithm and its Evaluation

This paper proposes a new lazy learning algorithm, named balanced-kNN, for high performance robust classification of noisy patterns. K-nearest neighbor (k-NN) is a simple and powerful method with a high accuracy for various real world applications using unbiased datasets. However, noisy datasets are...

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Bibliographic Details
Published inDenki Gakkai ronbunshi. D, Sangyō ōyō bumonshi Vol. 139; no. 2; p. 158
Main Authors Nagayama, Itaru, Miyahara, Akira, Shimabukuro, Koichi
Format Journal Article
LanguageEnglish
Japanese
Published Tokyo Japan Science and Technology Agency 01.02.2019
Subjects
Algorithms
Datasets
K-nearest neighbors algorithm
Machine learning
Online AccessGet full text
ISSN2187-1094
1348-8163
0913-6339
2187-1108
DOI10.1541/ieejias.139.158

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Summary:This paper proposes a new lazy learning algorithm, named balanced-kNN, for high performance robust classification of noisy patterns. K-nearest neighbor (k-NN) is a simple and powerful method with a high accuracy for various real world applications using unbiased datasets. However, noisy datasets are often gathered in real world applications. This paper presents a new robust algorithm, balanced-kNN, and compares the prediction accuracy with some conventional methods by using UCI datasets. The experimental results show that the balanced-kNN algorithm can perform more efficient classification of noisy data than the normal-kNN and weighted-kNN algorithms.
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ISSN:2187-1094
1348-8163
0913-6339
2187-1108
DOI:10.1541/ieejias.139.158