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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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
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