ANALYZING THE EFFECTS OF DATA IMBALANCE ON THE PERFORMANCE OF NEURAL NETWORKS IN MULTI-CLASS CLASSIFICATION TASKS
Keywords:
Data Imbalance, Neural Networks, Multi-Class Classification, Oversampling, Undersampling, Cost-Sensitive Learning, Data Augmentation, Model Performance, Precision-Recall, Machine LearningAbstract
This paper investigates the effects of data imbalance on the performance of neural networks in multi-class classification tasks. Data imbalance, where certain classes are underrepresented, poses significant challenges in training effective models, often leading to biased predictions and reduced overall accuracy. The study explores various strategies to mitigate these effects, including oversampling, undersampling, cost-sensitive learning, and data augmentation. By conducting experiments on real-world datasets, this research provides a comparative analysis of how these techniques influence the performance metrics such as accuracy, precision, and recall. The findings highlight the critical role of addressing data imbalance in enhancing the reliability of neural networks, particularly in scenarios involving complex multi-class classification tasks. The results suggest that while certain techniques offer substantial improvements, there remain challenges that warrant further investigation. This paper contributes to the ongoing discourse on optimizing neural networks for imbalanced data scenarios, offering practical insights for researchers and practitioners alike.
References
Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20-29.
Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249-259.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357.
Vinay SB (2024) AI-driven patent mining: unveiling innovation patterns through automated knowledge extraction. Int J Super AI (IJSAI) 1(1):1–11
Rajkumar AP, Kannan N (2013) Level of awareness of private players in the insurance industry: a study with special reference to Chennai City. Int J Manag (IJM) 2(2):113–124
Ramachandran KK (2024) The role of artificial intelligence in enhancing financial data security. Int J Artif Intell Appl (IJAIAP) 3(1):1–11
Palanisamy M, Tamilselvan N, Sivamani M (2013) Certain investigations on building e-contents for digital library database environment: an experimental approach. Int J Libr Inf Sci (IJLIS) 1(2):11–17
Drummond, C., & Holte, R. C. (2003). C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. Workshop on Learning from Imbalanced Datasets II, 1-8.
Elkan, C. (2001). The foundations of cost-sensitive learning. Proceedings of the 17th International Joint Conference on Artificial Intelligence, 973-978.
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011). A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, 42(4), 463-484.
He, H., & Ma, Y. (2013). Imbalanced Learning: Foundations, Algorithms, and Applications. Wiley-IEEE Press.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1-35.
Wong, S. C., Gatt, A., Stamatescu, V., & McDonnell, M. D. (2016). Understanding data augmentation for classification: When to warp?. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 1-6.
Zhou, Z. H., & Liu, X. Y. (2006). Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 18(1), 63-77.
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