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The eXtreme Gradient Boosting Method Optimized by Hybridized Sine Cosine Metaheuristics for Ship Vessel Classification

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Advances in Data-Driven Computing and Intelligent Systems (ADCIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 891))

Abstract

Ship classification is essential in coastal areas to ensure safety, protect the environment and improve maritime security. It also allows the optimization of resource allocation and can boost economic growth. Therefore, vessel identification is crucial to employ appropriate security measures. However, communication interruptions can happen during poor weather conditions, which could hinder the overall safety of vessels in the area. Security is, therefore, a main pivotal aspect that drives forward the vessel identification systems. This paper tackles this problem by proposing an XGBoost machine learning model that is optimized by an enhanced variant of the sine cosine metaheuristic algorithm that has the role of identifying and classifying naval vessels. The proposed method has been compared to the results attained by other cutting-edge metaheuristics algorithms, and experimental outcomes show that it obtained supreme results for this particular task.

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Notes

  1. 1.

    https://www.kaggle.com/datasets/eminserkanerdonmez/ais-dataset.

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Author information

Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11010, Belgrade, Serbia

    Milos Bukumira, Miodrag Zivkovic, Milos Antonijevic, Luka Jovanovic & Nebojsa Bacanin

  2. School of Electrical Engineering, 11000, Belgrade, Serbia

    Tamara Zivkovic

Authors
  1. Milos Bukumira
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  2. Miodrag Zivkovic
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  3. Milos Antonijevic
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  4. Luka Jovanovic
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  5. Nebojsa Bacanin
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  6. Tamara Zivkovic
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Corresponding author

Correspondence to Nebojsa Bacanin .

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Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  2. Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Sancoale, Goa, India

    Snehanshu Saha

  3. Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico

    Carlos A. Coello Coello

  4. Department of Mathematics, South Asian University, New Delhi, Madhya Pradesh, India

    Jagdish C. Bansal

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Bukumira, M., Zivkovic, M., Antonijevic, M., Jovanovic, L., Bacanin, N., Zivkovic, T. (2024). The eXtreme Gradient Boosting Method Optimized by Hybridized Sine Cosine Metaheuristics for Ship Vessel Classification. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-Driven Computing and Intelligent Systems. ADCIS 2023. Lecture Notes in Networks and Systems, vol 891. Springer, Singapore. https://doi.org/10.1007/978-981-99-9524-0_20

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  • DOI: https://doi.org/10.1007/978-981-99-9524-0_20

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