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Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection

Author

Listed:
  • Zhaobin Mo

    (Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA)

  • Xuan Di

    (Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA
    Data Science Institute, Columbia University, New York, NY 10027, USA)

  • Rongye Shi

    (Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA)

Abstract

How to sample training/validation data is an important question for machine learning models, especially when the dataset is heterogeneous and skewed. In this paper, we propose a data sampling method that robustly selects training/validation data. We formulate the training/validation data sampling process as a two-player game: a trainer aims to sample training data so as to minimize the test error, while a validator adversarially samples validation data that can increase the test error. Robust sampling is achieved at the game equilibrium. To accelerate the searching process, we adopt reinforcement learning aided Monte Carlo trees search (MCTS). We apply our method to a car-following modeling problem, a complicated scenario with heterogeneous and random human driving behavior. Real-world data, the Next Generation SIMulation (NGSIM), is used to validate this method, and experiment results demonstrate the sampling robustness and thereby the model out-of-sample performance.

Suggested Citation

  • Zhaobin Mo & Xuan Di & Rongye Shi, 2023. "Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection," Games, MDPI, vol. 14(1), pages 1-13, January.
    • Handle: RePEc:gam:jgames:v:14:y:2023:i:1:p:13-:d:1051349
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    References listed on IDEAS

    as
    1. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish, 2019. "Is more always better? The impact of vehicular trajectory completeness on car-following model calibration and validation," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 49-75.
    2. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
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