Research on an Adaptive Curriculum Learning Method for Imbalanced Tabular Data Classification
DOI:
https://doi.org/10.53469/wjimt.2025.08(04).08Keywords:
Machine learning, Imbalanced data, Curriculum learning, Tabular data classification, Sample difficulty modelingAbstract
In practical applications, tabular data commonly show a severe imbalance in class distribution, which causes great difficulties for traditional classification models in recognizing minority class samples. This study proposes an adaptive curriculum learning method based on sample difficulty modeling. The method ranks training samples accurately and applies a stage-wise weight control strategy to guide the model to learn progressively from easy to hard samples. Experiments conducted on several public tabular datasets, including Adult, Credit, and Census Income, show that the proposed method achieves improvements of 6.4% in F1 score and 5.1% in AUC compared with existing baseline algorithms. These results demonstrate the superior generalization ability and minority class recognition performance of the proposed method.
References
Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic markets, 26, 173-194.
Wang, Z., Yan, H., Wei, C., Wang, J., Bo, S., & Xiao, M. (2024, August). Research on autonomous driving decision-making strategies based deep reinforcement learning. In Proceedings of the 2024 4th International Conference on Internet of Things and Machine Learning (pp. 211-215).
Onyshchenko, S., Zhyvylo, Y., Cherviak, A., & Bilko, S. (2023). DETERMINING THE PATTERNS OF USING INFORMATION PROTECTION SYSTEMS AT FINANCIAL INSTITUTIONS IN ORDER TO IMPROVE THE LEVEL OF FINANCIAL SECURITY. Eastern-European Journal of Enterprise Technologies, 125(13).
Gao, D., Shenoy, R., Yi, S., Lee, J., Xu, M., Rong, Z., ... & Chen, Y. (2023). Synaptic resistor circuits based on Al oxide and Ti silicide for concurrent learning and signal processing in artificial intelligence systems. Advanced Materials, 35(15), 2210484.
Mo, K., Chu, L., Zhang, X., Su, X., Qian, Y., Ou, Y., & Pretorius, W. (2024). Dral: Deep reinforcement adaptive learning for multi-uavs navigation in unknown indoor environment. arXiv preprint arXiv:2409.03930.
Wang, S., Jiang, R., Wang, Z., & Zhou, Y. (2024). Deep learning-based anomaly detection and log analysis for computer networks. arXiv preprint arXiv:2407.05639.
Gong, C., Zhang, X., Lin, Y., Lu, H., Su, P. C., & Zhang, J. (2025). Federated Learning for Heterogeneous Data Integration and Privacy Protection.
Shih, K., Han, Y., & Tan, L. (2025). Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions.
Zhu, J., Ortiz, J., & Sun, Y. (2024, November). Decoupled Deep Reinforcement Learning with Sensor Fusion and Imitation Learning for Autonomous Driving Optimization. In 2024 6th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 306-310). IEEE.
Bao, Q., Chen, Y., & Ji, X. (2025). Research on evolution and early warning model of network public opinion based on online Latent Dirichlet distribution model and BP neural network. arXiv preprint arXiv:2503.03755.
Liu, Z., Costa, C., & Wu, Y. (2024). Data-Driven Optimization of Production Efficiency and Resilience in Global Supply Chains. Journal of Theory and Practice of Engineering Science, 4(08), 23-33.
Zhu, J., Sun, Y., Zhang, Y., Ortiz, J., & Fan, Z. (2024, October). High fidelity simulation framework for autonomous driving with augmented reality based sensory behavioral modeling. In IET Conference Proceedings CP989 (Vol. 2024, No. 21, pp. 670-674). Stevenage, UK: The Institution of Engineering and Technology.
Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M. E., & Stone, P. (2020). Curriculum learning for reinforcement learning domains: A framework and survey. Journal of Machine Learning Research, 21(181), 1-50.
Vepa, A., Yang, Z., Choi, A., Joo, J., Scalzo, F., & Sun, Y. (2024). Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation. Advances in Neural Information Processing Systems, 37, 71643-71671.
Li, Z., Ji, Q., Ling, X., & Liu, Q. (2025). A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games. Authorea Preprints.
Zhu, J., Wu, Y., Liu, Z., & Costa, C. (2025). Sustainable Optimization in Supply Chain Management Using Machine Learning. International Journal of Management Science Research, 8(1).
Feng, H. (2024, September). The research on machine-vision-based EMI source localization technology for DCDC converter circuit boards. In Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024) (Vol. 13275, pp. 250-255). SPIE.
Zhang, W., Li, Z., & Tian, Y. (2025). Research on Temperature Prediction Based on RF-LSTM Modeling. Authorea Preprints.
Li, Z. (2024). Advances in Deep Reinforcement Learning for Computer Vision Applications. Journal of Industrial Engineering and Applied Science, 2(6), 16-26.
Liu, J., Li, K., Zhu, A., Hong, B., Zhao, P., Dai, S., ... & Su, H. (2024). Application of deep learning-based natural language processing in multilingual sentiment analysis. Mediterranean Journal of Basic and Applied Sciences (MJBAS), 8(2), 243-260.
Tang, X., Wang, Z., Cai, X., Su, H., & Wei, C. (2024, August). Research on heterogeneous computation resource allocation based on data-driven method. In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS) (pp. 916-919). IEEE.
Fernando, K. R. M., & Tsokos, C. P. (2021). Dynamically weighted balanced loss: class imbalanced learning and confidence calibration of deep neural networks. IEEE Transactions on Neural Networks and Learning Systems, 33(7), 2940-2951.
Zhu, J., Xu, T., Zhang, Y., & Fan, Z. (2024). Scalable Edge Computing Framework for Real-Time Data Processing in Fintech Applications. International Journal of Advance in Applied Science Research, 3, 85-92.
Liu, Z., Costa, C., & Wu, Y. (2024). Leveraging Data-Driven Insights to Enhance Supplier Performance and Supply Chain Resilience.
Aldeer, M., Sun, Y., Pai, N., Florentine, J., Yu, J., & Ortiz, J. (2023, May). A Testbed for Context Representation in Physical Spaces. In Proceedings of the 22nd International Conference on Information Processing in Sensor Networks (pp. 336-337).
Feng, H. (2024). High-Efficiency Dual-Band 8-Port MIMO Antenna Array for Enhanced 5G Smartphone Communications. Journal of Artificial Intelligence and Information, 1, 71-78.
Liu, Z., Costa, C., & Wu, Y. (2024). Quantitative Assessment of Sustainable Supply Chain Practices Using Life Cycle and Economic Impact Analysis.
Yang, J., Chen, T., Qin, F., Lam, M. S., & Landay, J. A. (2022, April). Hybridtrak: Adding full-body tracking to vr using an off-the-shelf webcam. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
Wang, G., Qin, F., Liu, H., Tao, Y., Zhang, Y., Zhang, Y. J., & Yao, L. (2020). MorphingCircuit: An integrated design, simulation, and fabrication workflow for self-morphing electronics. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(4), 1-26.
Bolón-Canedo, V., Sánchez-Maroño, N., & Alonso-Betanzos, A. (2016). Feature selection for high-dimensional data. Progress in Artificial Intelligence, 5, 65-75.
Pes, B. (2017, June). Feature selection for high-dimensional data: the issue of stability. In 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) (pp. 170-175). IEEE.