JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v10i2.13508ArticleIVY-RF-Based Logistics Claims Risk Classification and Predictionhttps://artdesignp.com/journal/JERA/10/2/10.26689/jera.v10i2.13508WangShufeng20261022026-03-27With the rapid development of e-commerce, the scale and complexity of logistics operations continue to increase, and the claims risks caused by abnormal events such as cargo damage, delays, and loss during transportation are becoming increasingly prominent. The traditional claims processing model, primarily based on manual review, is no longer adequate for the high-frequency, large-scale business demands in terms of processing efficiency, decision consistency, and cost control. There is an urgent need to introduce intelligent methods to achieve accurate identification and hierarchical management of claims risks. Addressing the challenges of diverse feature dimensions, highly imbalanced category distribution, and difficulty in distinguishing different risk types in logistics claims data, this paper proposes a Random Forest Logistics Claims Risk Classification Model (IVY-RF) based on the IVY growth optimization algorithm. This method uses a random forest as the basic classifier, fully leveraging its advantages in nonlinear relationship modeling and feature interaction capture. It also introduces the IVY metaheuristic optimization algorithm to adaptively optimize the model’s key hyperparameters globally. Experimental results based on real-world logistics claims datasets demonstrate that the IVY-RF model significantly outperforms comparable models such as IVY-LightGBM and IVY-XGBoost in core evaluation metrics, including macro-average F1 score, weighted precision, and weighted recall, achieving a better performance balance between the majority and minority high-risk categories. The findings indicate that the proposed IVY-RF model exhibits significant advantages in prediction accuracy, stability, and engineering feasibility, providing reliable technical support for logistics companies to conduct intelligent identification and refined management of claims risks.

Govindan K, Soleimani H, 2017, A Review of Reverse Logistics and Closed-Loop Supply Chains. Journal of Cleaner Production, 2017(142): 371–384.

Berg C, Deichmann U, Liu Y, et al., 2017, Transport Policies and Development. The Journal of Development Studies, 53(4): 465–480.

Van Hoek R, 2020, Research Opportunities for a More Resilient Post-COVID-19 Supply Chain: Closing the Gap between Research Findings and Industry Practice. International Journal of Operations & Production Management, 40(4): 341–355.

Gao Y, Jiang Y, Peng Y, et al., 2025, Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods. Tomography, 11(5): 52.

Koukopoulos A, Sani G, 2014, DSM-5 Criteria for Depression with Mixed Features: A Farewell to Mixed Depression. Acta Psychiatrica Scandinavica, 129(1): 4–16.

Yuan F, Zuo Z, Jiang Y, et al., 2025, AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection. Algorithms, 18(5): 263.

Song Y, Lu Y, 2015, Decision Tree Methods: Applications for Classification and Prediction. Shanghai Archives of Psychiatry, 2015.

Saraswat B, Singhal A, Agarwal S, et al., 2023, Insurance Claim Analysis Using Traditional Machine Learning Algorithms, 2023 International Conference on Disruptive Technologies.

Miao T, Zhang S, Zhang Y, et al., 2024, Combined DeRitis Ratio and Alkaline Phosphatase on The Prediction of Portal Vein Tumor Thrombosis in Patients with Hepatocellular Carcinoma. Med. Public and Global Health.

Zhang J, Li Z, Luo X, et al., 2024, Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction. Applied Sciences, 2024.

Lin Y, Xu X, Chen S, 2024, Construction of Nomogram Based on Clinical Factors for The Risk Prediction of Postoperative Complications in Children with Choledochal Cyst. Frontiers in Pediatrics, 2024.

Zhang K, Yuan F, Jiang Y, et al., 2025, A Particle Swarm Optimization-Guided Ivy Algorithm for Global Optimization Problems. Biomimetics, 10(5): 342.

Ghasemi M, Zare M, Trojovský P, et al., 2024, Optimization based on the Smart Behavior of Plants with its Engineering Applications: Ivy Algorithm. Knowledge-Based Systems, 2024(295): 111850.

Padon O, McMillan K, Panda A, et al., 2016, Ivy: Safety Verification by Interactive Generalization, Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation, 614–630.

Belgiu M, Drăguţ L, 2016, Random Forest in Remote Sensing: A Review of Applications and Future Directions. ISPRS Journal of Photogrammetry and Remote Sensing, 2016(114): 24–31.

Wang K, Truong K, 2025, A Video-Based Assessment Tool Using Machine Learning for Ergonomic Risk Prediction in Manual Lifting Tasks. Ergonomics, 2025.

Ouyang H, Li W, Gao F, et al., 2024, Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF. Energies (19961073), 17(22).

Huang S, Li C, Zhou J, et al., 2025, Use of BOIvy Optimization Algorithm-Based Machine Learning Models in Predicting the Compressive Strength of Bentonite Plastic Concrete. Materials, 2025.