Popukaylo Vladimir Sergeevich (A Doctor of Philosophy in Informatics, Associate Professor of Information Technology, Shevchenko State University of Pridnestrovie.)
Shmelyova Anastasiya Vladimirovna (Postgraduate student, Shevchenko State University of Pridnestrovie )
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The article addresses the problem of using imbalanced data in multi-class classification tasks. It briefly examines the main existing approaches and proposes the application of the multidimensional kernel density estimation method to balance classes. The algorithm for applying this method is described, and an experiment is conducted using synthetic data. The results are compared with existing algorithms such as random oversampling of the small class, ADASYN, SMOTE, ASMO, SVMSMOTE. The article shows the possibility of using the multidimensional kernel density estimation method in principle to improve the quality of machine learning algorithms in conditions of imbalanced data
Keywords:Machine Learning, classification task, tabular data processing, imbalanced data, multidimensional kernel density estimation method
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Citation link:
Popukaylo V. S., Shmelyova A. V. APPLICATION OF THE MULTIDIMENSIONAL KERNEL DENSITY ESTIMATION METHOD IN MACHINE LEARNING TASKS WITH IMBALANCED DATA // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2023. -№08/2. -С. 128-131 DOI 10.37882/2223-2966.2023.8-2.29 |
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