Oleksandr Vlasyuk
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Oleksandr Vlasyuk has a total of 1 co-authors. Here are the top co-authors by number of shared publications:
- Viktor Khavalko
Lviv Polytechnic National University, Lviv, Ukraine, Ukraine
1 shared publication
Latest Publications
- 16 October 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Methods for Managing Class Imbalance in Machine Learning Tasks
Class imbalance is a prevalent challenge in machine learning, particularly in domains like credit risk assessment, fraud detection, and medical diagnosis, where the minority class often represents the critical cases of interest. Traditional machine learning models tend to be biased towards the majority class, leading to suboptimal performance in detecting minority class instances. This paper explores various strategies to mitigate the effects of class imbalance, including performance metric adjustments, resampling techniques such as oversampling, undersampling, and Synthetic Minority oversampling Technique (SMOTE), as well as algorithmic modifications. We propose different hybrid approaches that combines Random Forest with Laplace regularization and others to enhance the classification of minority classes in imbalanced datasets. Our approach is validated using a credit risk dataset, where the proposed method demonstrates significant improvements in recall and F1 scores compared to baseline models, suggesting its effectiveness in addressing class imbalance in critical applications.
- 16 October 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Methods for Managing Class Imbalance in Machine Learning Tasks
Class imbalance is a prevalent challenge in machine learning, particularly in domains like credit risk assessment, fraud detection, and medical diagnosis, where the minority class often represents the critical cases of interest. Traditional machine learning models tend to be biased towards the majority class, leading to suboptimal performance in detecting minority class instances. This paper explores various strategies to mitigate the effects of class imbalance, including performance metric adjustments, resampling techniques such as oversampling, undersampling, and Synthetic Minority oversampling Technique (SMOTE), as well as algorithmic modifications. We propose different hybrid approaches that combines Random Forest with Laplace regularization and others to enhance the classification of minority classes in imbalanced datasets. Our approach is validated using a credit risk dataset, where the proposed method demonstrates significant improvements in recall and F1 scores compared to baseline models, suggesting its effectiveness in addressing class imbalance in critical applications.