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Machine-Learning Methods on Noisy and Sparse Data

Author

Listed:
  • Konstantinos Poulinakis

    (University of Nicosia, Nicosia CY-2417, Cyprus)

  • Dimitris Drikakis

    (University of Nicosia, Nicosia CY-2417, Cyprus)

  • Ioannis W. Kokkinakis

    (University of Nicosia, Nicosia CY-2417, Cyprus)

  • Stephen Michael Spottswood

    (Air Force Research Laboratory, Wright Patterson AFB, Greene County, OH 45433-7402, USA)

Abstract

Experimental and computational data and field data obtained from measurements are often sparse and noisy. Consequently, interpolating unknown functions under these restrictions to provide accurate predictions is very challenging. This study compares machine-learning methods and cubic splines on the sparsity of training data they can handle, especially when training samples are noisy. We compare deviation from a true function f using the mean square error, signal-to-noise ratio and the Pearson R 2 coefficient. We show that, given very sparse data, cubic splines constitute a more precise interpolation method than deep neural networks and multivariate adaptive regression splines. In contrast, machine-learning models are robust to noise and can outperform splines after a training data threshold is met. Our study aims to provide a general framework for interpolating one-dimensional signals, often the result of complex scientific simulations or laboratory experiments.

Suggested Citation

  • Konstantinos Poulinakis & Dimitris Drikakis & Ioannis W. Kokkinakis & Stephen Michael Spottswood, 2023. "Machine-Learning Methods on Noisy and Sparse Data," Mathematics, MDPI, vol. 11(1), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:1:p:236-:d:1023270
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    References listed on IDEAS

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    1. Chang-Jui Lin & Hsueh-Fang Chen & Tian-Shyug Lee, 2011. "Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 2(2), pages 14-24, May.
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    1. Luke T. Woods & Zeeshan A. Rana, 2023. "Modelling Sign Language with Encoder-Only Transformers and Human Pose Estimation Keypoint Data," Mathematics, MDPI, vol. 11(9), pages 1-28, May.
    2. Wu, Menglong & Xiong, Jiajie & Li, Ruoyu & Dong, Aihong & Lv, Chang & Sun, Dan & Abdelghany, Ahmed Elsayed & Zhang, Qian & Wang, Yaqiong & Siddique, Kadambot H.M. & Niu, Wenquan, 2024. "Precision forecasting of fertilizer components’ concentrations in mixed variable-rate fertigation through machine learning," Agricultural Water Management, Elsevier, vol. 298(C).

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