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http://dx.doi.org/10.5351/KJAS.2021.34.3.401

Random projection ensemble adaptive nearest neighbor classification  

Kang, Jongkyeong (Department of Statistics, Korea University)
Jhun, Myoungshic (Department of Applied Mathematics and Statistics, The State University of New York Korea)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.3, 2021 , pp. 401-410 More about this Journal
Abstract
Popular in discriminant classification analysis, k-nearest neighbor classification methods have limitations that do not reflect the local characteristic of the data, considering only the number of fixed neighbors. Considering the local structure of the data, the adaptive nearest neighbor method has been developed to select the number of neighbors. In the analysis of high-dimensional data, it is common to perform dimension reduction such as random projection techniques before using k-nearest neighbor classification. Recently, an ensemble technique has been developed that carefully combines the results of such random classifiers and makes final assignments by voting. In this paper, we propose a novel discriminant classification technique that combines adaptive nearest neighbor methods with random projection ensemble techniques for analysis on high-dimensional data. Through simulation and real-world data analyses, we confirm that the proposed method outperforms in terms of classification accuracy compared to the previously developed methods.
Keywords
adaptive nearest neighbor; classification; high-dimensional data; K-nearest neighbor; random projection;
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Times Cited By KSCI : 1  (Citation Analysis)
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