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A novel outlier-insensitive local support vector machine for robust data-driven forecasting in engineering

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Abstract

Machine learning (ML)-based data-driven methods have promoted the progress of modeling in many engineering domains. These methods can achieve high prediction and generalization performance for large, high-quality datasets. However, ML methods can yield biased predictions if the observed data (i.e., response variable y) are corrupted by outliers. This paper addresses this problem with a novel, robust ML approach that is formulated as an optimization problem by coupling locally weighted least-squares support vector machines for regression (LWLS-SVMR) with one weight function. The weight is a function of residuals and allows for iteration within the proposed approach, significantly reducing the negative interference of outliers. A new efficient hybrid algorithm is developed to solve the optimization problem. The proposed approach is assessed and validated by comparison with relevant ML approaches on both one-dimensional simulated datasets corrupted by various outliers and multi-dimensional real-world engineering datasets, including datasets used for predicting the lateral strength of reinforced concrete (RC) columns, the fuel consumption of automobiles, the rising time of a servomechanism, and dielectric breakdown strength. Finally, the proposed method is applied to produce a data-driven solver for computational mechanics with a nonlinear material dataset corrupted by outliers. The results all show that the proposed method is robust against non-extreme and extreme outliers and improves the predictive performance necessary to solve various engineering problems.

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Data availability

The data that support the findings of this study are openly available in UCI machine learning repository at https://archive.ics.uci.edu/ml/index.php.

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Acknowledgements

This material is based in part on work supported by the Natural Science Foundation of Hubei Province under Grant #2022CFB294, the National Natural Science Foundation of China under Grant #52208485 and the National Science Foundation under Grant CMMI #1944301. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Natural Science Foundation of Hubei Province, National Natural Science Foundation of China and National Science Foundation.

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Authors and Affiliations

  1. College of Civil Engineering and Architecture, China Three Gorges University, Yichang, 443002, Hubei, China

    Huan Luo

  2. Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, 77843, USA

    Stephanie German Paal

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  1. Huan Luo
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Correspondence to Huan Luo.

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Luo, H., Paal, S.G. A novel outlier-insensitive local support vector machine for robust data-driven forecasting in engineering. Engineering with Computers 39, 3671–3689 (2023). https://doi.org/10.1007/s00366-022-01781-9

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