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.
CITATION STYLE
Hayashi, S., Prasasti, N., Kanamori, K., & Ohwada, H. (2016). Improving behavior prediction accuracy by using machine learning for agent-based simulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9621, pp. 280–289). Springer Verlag. https://doi.org/10.1007/978-3-662-49381-6_27
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