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Improving the predictive ability of multivariate calibration models using support vector data description

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  • Walid Gani

Abstract

Outlier detection is a crucial step in building multivariate calibration models and enhancing their predictive ability. However, traditional outlier detection methods often suffer from important drawbacks mainly their reliance on assumptions about the data model distribution and their unsuitability for real-life applications. This paper investigates the use of support vector data description (SVDD) for the detection of outliers and proposes a multivariate calibration strategy that combines partial least squares (PLS) and SVDD. For the assessment of the proposed calibration strategy, an experimental study aiming to predict four properties of diesel fuel is conducted. The results show that the predictive ability of PLS-SVDD is better than the predictive ability of a classical strategy that combines PLS and the T2 method.

Suggested Citation

  • Walid Gani, 2021. "Improving the predictive ability of multivariate calibration models using support vector data description," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 13(3), pages 227-243.
    • Handle: RePEc:ids:injdan:v:13:y:2021:i:3:p:227-243
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