Abstract
This paper simulated the missile engagement situation using a simulator and conducted a machine learning study based on the generated data. The simulator simulates missile engagements between the enemy and our forces and collects data. The collected data is learned using random forest, XGBoost, and LGBM models after preprocessing. In addition, hyperparameter adjustments were performed for each model to find the optimal parameters. Different metrics for accuracy, F1-score, and ROC-AUC were used for performance comparison. As a result of the experiment, XGBoost showed the best performance in performance indicators, and LGBM was the fastest in terms of learning speed. This paper suggests that XGBoost, which is slow in learning speed but has the best accuracy and performance indicators, is suitable for one-to-one interception situations, and LGBM, which is fast in learning and has excellent performance indicators, is suitable for many-to-many interception situations.
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Acknowledgement
This work was supported partly by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2020-0-00107, Development of the technology to automate the recommendations for big data analytic models that define data characteristics and problems), and partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1A2C1009894).
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Jin, S., Dahouda, M.K., Joe, I. (2023). Ensemble Machine Learning Models for Simulating the Missile Defense System. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_12
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DOI: https://doi.org/10.1007/978-3-031-21438-7_12
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