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Time Series Data Imbalance Resolution Techniques for Early Prediction
Eungseon An, Taehyoung Kwon, Doguk Kim
http://doi.org/10.5626/JOK.2025.52.7.593
Time series forecasting is a critical task that involves analyzing observed time series data to predict future values. However, when dealing with imbalanced data, model performance can degrade, leading to biased predictions. Although recent studies have explored various deep learning techniques and data augmentation methods, many fail to address challenges posed by data imbalance and the intrinsic characteristics of time series data simultaneously, leaving underlying issues unresolved. This study proposed a novel approach that could leverage temporal patterns to generate synthetic samples and extend the scope of early prediction. By identifying key moments that could effectively distinguish between positive and negative classes, our method enhanced the ability to predict further into the future. The method proposed in this study demonstrated superior performance to existing methods and proved the feasibility of early prediction for longer time lags.
Semi-Supervised Object Detection for Small Imbalanced Drama Dataset
http://doi.org/10.5626/JOK.2024.51.11.978
Images of the theme of a drama are typically zoomed-in mainly to people. As a result, people-oriented images are predominant in drama data, and class imbalance naturally occurs. This paper addresses the issue of class imbalance in drama data for object detection tasks and proposes various sampling methods to tackle this challenge within the framework of semi-supervised learning. Experimental evaluations demonstrated that the suggested semi-supervised learning approach with specialized sampling methods outperformed traditional supervised and semi-supervised methods. This study underscores the significance of selecting appropriate training data and sampling methods to optimize object detection performance in specialized datasets with unique characteristics.
A Study on Development of Technology to Improve Imbalanced Data Problems in Numerical Dataset Using Tomek Links Method combined with Balancing GAN
Hyunsik Na, Sohee Park, Daeseon Choi
http://doi.org/10.5626/JOK.2020.47.10.974
Machine Learning is useful due to its good performance and application in various fields such as data classification, voice recognition and predictive models. However, there exists a problem regarding the imbalance between classes in the training dataset, which degrades the classification performance of the minority class. In this paper, we propose a new data augmentation method that combines the Balancing GAN and Tomek Links Method to solve the Imbalanced Data problem and find a clear decision boundary. To verity the proposed method, we have evaluated the performance according to the classification model using five datasets. Moreover, the performance has been compared with Data Sampling and GAN based Data Augmentation Techniques. The results showed that the classification performance was improved or maintained by 0.05～0.195 in 17 of the total 25 performance evaluations. The method proposed in this paper showed the potential as a new method to solve the Imbalanced Data problem.
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