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Classification Methods for MOBA Games

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17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

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Abstract

The rise of the sports industry, which over time has increased in popularity along with machine learning and the possibilities for improving upon previously known and used methods, can serve many future predictions and benefits. This paper proposes a methodology to feature sorting in the context of supervised machine learning algorithms. A new perspective on machine learning by using it to predict outcomes with a database of the popular moba game Dota2, which consists of a large volume of data that was collected and analyzed. The reported results are concerned with three machine learning models with two significant metrics such as F-measure and Accuracy.

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Acknowledgements

This work has been partially subsidised by the project US-1263341 (Junta de Andalucía) and FEDER funds.

Author information

Authors and Affiliations

  1. International University of Andalusia, Huelva, Spain

    Marco A. Peña-Cubillos & Alejandro Villar-Ruiz

  2. Department of Electronic, Computer Systems and Automation Engineering, University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  3. Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, China

    Yaoyang Wu & Simon Fong

Authors
  1. Marco A. Peña-Cubillos
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  2. Alejandro Villar-Ruiz
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  3. Antonio J. Tallón-Ballesteros
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  4. Yaoyang Wu
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  5. Simon Fong
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Corresponding author

Correspondence to Antonio J. Tallón-Ballesteros .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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Peña-Cubillos, M.A., Villar-Ruiz, A., Tallón-Ballesteros, A.J., Wu, Y., Fong, S. (2023). Classification Methods for MOBA Games. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_55

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