Improving learning vector quantization using data reduction
International Journal of Advances in Intelligent Informatics
View Publication Info| Field | Value | |
| Title |
Improving learning vector quantization using data reduction
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| Creator |
Semadi, Pande Nyoman Ariyuda
Pulungan, Reza |
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| Subject |
Learning vector quantization; Data reduction; Geometric proximity; Euclidean distance
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| 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.
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| Publisher |
Universitas Ahmad Dahlan
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| Contributor |
—
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| Date |
2019-11-22
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion — |
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| Format |
application/pdf
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| Identifier |
http://ijain.org/index.php/IJAIN/article/view/330
10.26555/ijain.v5i3.330 |
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| Source |
International Journal of Advances in Intelligent Informatics; Vol 5, No 3 (2019): November 2019; 218-229
2548-3161 2442-6571 |
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| Language |
eng
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| Relation |
http://ijain.org/index.php/IJAIN/article/view/330/ijain_v5i3_p218-229
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| Rights |
https://creativecommons.org/licenses/by-sa/4.0
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