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Analysis of Learning Influence of Training Data Selected by Distribution Consistency
This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we select data based on the distributio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913647/ https://www.ncbi.nlm.nih.gov/pubmed/33557021 http://dx.doi.org/10.3390/s21041045 |
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author | Hwang, Myunggwon Jeong, Yuna Sung, Won-Kyung |
author_facet | Hwang, Myunggwon Jeong, Yuna Sung, Won-Kyung |
author_sort | Hwang, Myunggwon |
collection | PubMed |
description | This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we select data based on the distribution consistency (DC) of the target class data and examine how it affects the classifier. We use CIFAR-10 for the experiment and set various grid ratios from 0.5 to 0.005. The influences of these variables were analyzed with the use of different training data sizes selected based on high-DC, low-DC (inverse of high DC), and random (no criteria) selections. As a result, the average point accuracy at 0.95% (±0.65) and the point accuracy at 1.54% (±0.59) improved for the grid configurations of 0.008 and 0.005, respectively. These outcomes justify an improved performance compared with that of the existing approach (data distribution search). In this study, we confirmed that the learning performance improved when the training data were selected for very small grid and high-DC settings. |
format | Online Article Text |
id | pubmed-7913647 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79136472021-02-28 Analysis of Learning Influence of Training Data Selected by Distribution Consistency Hwang, Myunggwon Jeong, Yuna Sung, Won-Kyung Sensors (Basel) Article This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we select data based on the distribution consistency (DC) of the target class data and examine how it affects the classifier. We use CIFAR-10 for the experiment and set various grid ratios from 0.5 to 0.005. The influences of these variables were analyzed with the use of different training data sizes selected based on high-DC, low-DC (inverse of high DC), and random (no criteria) selections. As a result, the average point accuracy at 0.95% (±0.65) and the point accuracy at 1.54% (±0.59) improved for the grid configurations of 0.008 and 0.005, respectively. These outcomes justify an improved performance compared with that of the existing approach (data distribution search). In this study, we confirmed that the learning performance improved when the training data were selected for very small grid and high-DC settings. MDPI 2021-02-04 /pmc/articles/PMC7913647/ /pubmed/33557021 http://dx.doi.org/10.3390/s21041045 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hwang, Myunggwon Jeong, Yuna Sung, Won-Kyung Analysis of Learning Influence of Training Data Selected by Distribution Consistency |
title | Analysis of Learning Influence of Training Data Selected by Distribution Consistency |
title_full | Analysis of Learning Influence of Training Data Selected by Distribution Consistency |
title_fullStr | Analysis of Learning Influence of Training Data Selected by Distribution Consistency |
title_full_unstemmed | Analysis of Learning Influence of Training Data Selected by Distribution Consistency |
title_short | Analysis of Learning Influence of Training Data Selected by Distribution Consistency |
title_sort | analysis of learning influence of training data selected by distribution consistency |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913647/ https://www.ncbi.nlm.nih.gov/pubmed/33557021 http://dx.doi.org/10.3390/s21041045 |
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