@article{ART003374427},
author={Hyun-Seob Lee},
title={A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2026},
volume={12},
number={4},
pages={2}
TY - JOUR
AU - Hyun-Seob Lee
TI - A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments
JO - Journal of Internet of Things and Convergence
PY - 2026
VL - 12
IS - 4
PB - The Korea Internet of Things Society
SP - 2
EP -
SN - 2466-0078
AB - The proliferation of the Internet of Things (IoT) environment has led to the generation of diverse sensor-based data, and research on intelligent classification and prediction models utilizing this data is actively underway. However, in real IoT application environments, acquiring large-scale data is often difficult due to factors such as data collection costs, security concerns, and equipment limitations. In these small-scale data environments, it is challenging to meet the vast training data requirements of deep learning-based models, and overfitting issues can become severe. The objective of this study is to experimentally analyze the generalization performance and stability of Random Forest in small-scale IoT data environments. To achieve this, comparative experiments are conducted using the Small IoT Dataset provided by Kaggle, evaluating Random Forest, Support Vector Machine (SVM), and neural network models. Specifically, by applying cross-validation-based evaluation instead of a single training-validation split, we quantitatively compare the generalization capabilities of each model by analyzing not only their average performance but also their performance variance. This analysis is expected to contribute to the application of machine learning in rapidly changing small-scale IoT environments.
KW - IoT;Small Dataset;Machine Learning;Data Poverty;Overfitting Prevention
DO -
UR -
ER -
Hyun-Seob Lee. (2026). A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments. Journal of Internet of Things and Convergence, 12(4), 2.
Hyun-Seob Lee. 2026, "A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments", Journal of Internet of Things and Convergence, vol.12, no.4 2.
Hyun-Seob Lee "A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments" Journal of Internet of Things and Convergence 12.4 2 (2026) : 2.
Hyun-Seob Lee. A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments. 2026; 12(4), 2.
Hyun-Seob Lee. "A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments" Journal of Internet of Things and Convergence 12, no.4 (2026) : 2.
Hyun-Seob Lee. A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments. Journal of Internet of Things and Convergence, 12(4), 2.
Hyun-Seob Lee. A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments. Journal of Internet of Things and Convergence. 2026; 12(4) 2.
Hyun-Seob Lee. A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments. 2026; 12(4), 2.
Hyun-Seob Lee. "A Comparative Study on the Generalization Performance of Machine Learning Model in Small-Scale Data Environments" Journal of Internet of Things and Convergence 12, no.4 (2026) : 2.
