Algorithms for calculating estimates based on minimizing errors in object classification
Abstract
In this study, the distribution of real objects and synthetically augmented classes was analyzed, and their impact on machine learning models was evaluated. The training outcomes of logistic regression, decision trees, random forest, and SVM models using synthetic data were compared with those trained on real object datasets. Experimental results demonstrated that the use of synthetically augmented data improves the classification model's accuracy, with particularly significant enhancements observed in the performance of certain algorithms
About the Authors
List of references
He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS).
Fernández, A., García, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for Learning from Imbalanced Data: Progress and Challenges. Journal of Artificial Intelligence Review.
Liu, B., Ding, H., & Wang, Y. (2020). Synthetic Data Augmentation for Medical Diagnosis. IEEE Transactions on Biomedical Engineering.
Smith, J. (2020). Medical Classification Systems. Journal of AI in Medicine.
Brown, R. (2019). Heuristic Algorithms in Diagnosis. Medical Informatics Review.
Nishanov, A., Ruzibaev, O., & Tran, N. (2016). Modification of decision rules “ball Apolonia” for the problem of classification. 2016 International Conference on Information Science and Communications Technologies (ICISCT). https://doi.org/10.1109/ICISCT.2016.7777382
Nishanov, A., Saidrasulov, Sh., & Babadjanov, E. (2022). Analysis of Methodology of Rating Evaluation of Digital Economy and E-Government Development in Uzbekistan. International Journal of Early Childhood Special Education, 14(2), 2447–2452. https://doi.org/10.9756/INT-JECSE/V14I2.230
Nishanov, A., Ruzibaev, O., Chedjou, J. C., Kyamakya, K., Abhiram, K., De Silva, Djurayev, G., & Khasanova, M. (2020). Algorithm for the Selection of Informative Symptoms in the Classification of Medical Data. Developments of Artificial Intelligence Technologies in Computation and Robotics, 12, 647–658. https://doi.org/10.1142/9789811223334_0078
Nishanov, A. Kh., Turakulov, A. Kh., & Turakhanov, Kh. V. (1999). Reshaiushchee pravilo dlia klassifikatsii patologii zritel'noĭ sistemy [A Decisive Rule in Classifying Diseases of the Visual System]. Med Tekh, (4), 16–18. [In Russian]. PMID: 10464756.
Nishanov, A., Akbarova, M., Tursunov, A., Ollamberganov, F., & Rashidova, D. (2024). Clustering Algorithm Based on Object Similarity. Journal of Mathematics, Mechanics and Computer Science / Computer Science, 123(3), 108–120. https://doi.org/10.26577/JMMCS2024-v123-i3-4