@article{ART003353171},
author={Hyun-Seob Lee},
title={A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={3},
pages={3}
TY - JOUR
AU - Hyun-Seob Lee
TI - A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 3
PB - The Korea Internet of Things Society
SP - 3
EP -
SN - 2466-0078
AB - Recent advances in artificial intelligence technology have driven the development of automated systems across various fields. However, acquiring large-scale, high-quality data required for training high-performance machine learning models demands significant costs and time. Particularly in specific domains or resource-constrained environments, the amount of collectable data is limited, leading to serious issues such as model overfitting and reduced generalization performance. This study proposes a data management and learning policy that systematically applies data augmentation techniques to address this data scarcity problem. The proposed method enhances the diversity of the training dataset by applying various augmentation techniques, such as geometric transformations and noise insertion, based on the limited original data. This approach overcomes the ‘curse of dimensionality’ and improves the model's prediction accuracy. This study demonstrates that efficient machine learning implementation is achievable even in environments with limited data resources through data augmentation policies. This is expected to provide a practical solution for researchers with restricted access to high-performance computing infrastructure or large-scale datasets.
KW - Data Augmentation;Machine Learning;Data Poverty;Overfitting Prevention;Data Preprocessing
DO -
UR -
ER -
Hyun-Seob Lee. (2026). A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments. Journal of Internet of Things and Convergence, 12(3), 3.
Hyun-Seob Lee. 2026, "A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments", Journal of Internet of Things and Convergence, vol.12, no.3 3.
Hyun-Seob Lee "A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments" Journal of Internet of Things and Convergence 12.3 3 (2026) : 3.
Hyun-Seob Lee. A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments. 2026; 12(3), 3.
Hyun-Seob Lee. "A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments" Journal of Internet of Things and Convergence 12, no.3 (2026) : 3.
Hyun-Seob Lee. A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments. Journal of Internet of Things and Convergence, 12(3), 3.
Hyun-Seob Lee. A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments. Journal of Internet of Things and Convergence. 2026; 12(3) 3.
Hyun-Seob Lee. A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments. 2026; 12(3), 3.
Hyun-Seob Lee. "A Data Augmentation-Based Learning Policy for Improving Machine Learning Performance in Data-Scarce Environments" Journal of Internet of Things and Convergence 12, no.3 (2026) : 3.
