Improving learning vector quantization using data reduction

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Improving learning vector quantization using data reduction
 
Creator Semadi, Pande Nyoman Ariyuda
Pulungan, Reza
 
Subject Learning vector quantization; Data reduction; Geometric proximity; Euclidean distance
 
Description Learning Vector Quantization (LVQ) is a supervised learning algorithm commonly used for statistical classification and pattern recognition. The competitive layer in LVQ studies the input vectors and classifies them into the correct classes. The amount of data involved in the learning process can be reduced by using data reduction methods. In this paper, we propose a data reduction method that uses geometrical proximity of the data. The basic idea is to drop sets of data that have many similarities and keep one representation for each set. By certain adjustments, the data reduction methods can decrease the amount of data involved in the learning process while still maintain the existing accuracy. The amount of data involved in the learning process can be reduced down to 33.22% for the abalone dataset and 55.02% for the bank marketing dataset, respectively.
 
Publisher Universitas Ahmad Dahlan
 
Contributor
 
Date 2019-11-22
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/330
10.26555/ijain.v5i3.330
 
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 3 (2019): November 2019; 218-229
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/330/ijain_v5i3_p218-229
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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