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Improving Behavior Prediction Accuracy by Using Machine Learning for Agent-Based Simulation

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Intelligent Information and Database Systems (ACIIDS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9621))

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Abstract

This study models an integration between agent-based simulation and machine learning in order to achieve comprehensive behavior prediction. The model is applied to the case of customer churning in a subscription-based business. Providing a good model for behavior prediction requires dynamic simulation based on social structure. In this study, we first executed an agent-based simulation to capture the dynamic structure of human behavior. Next, we conducted machine learning to classify human behavior using a classification algorithm. Finally, we verified the agent-based simulation and machine learning results by comparing the accuracy of both models. Based on the agent-based simulation results, we provide some recommendations to improve the accuracy of agent-based simulation based on the classification results from machine-learning procedures.

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Author information

Authors and Affiliations

  1. Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science, Tokyo, Japan

    Shinji Hayashi, Niken Prasasti, Katsutoshi Kanamori & Hayato Ohwada

Authors
  1. Shinji Hayashi
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  2. Niken Prasasti
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  3. Katsutoshi Kanamori
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  4. Hayato Ohwada
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Corresponding author

Correspondence to Shinji Hayashi .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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© 2016 Springer-Verlag Berlin Heidelberg

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Hayashi, S., Prasasti, N., Kanamori, K., Ohwada, H. (2016). Improving Behavior Prediction Accuracy by Using Machine Learning for Agent-Based Simulation. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_27

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  • DOI: https://doi.org/10.1007/978-3-662-49381-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49380-9

  • Online ISBN: 978-3-662-49381-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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