Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis

• Published in: Expert systems with applications Vol. 189; p. 115691
• Main Authors: Zouhri, Wahb, Homri, Lazhar, Dantan, Jean-Yves
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.03.2022; Elsevier BV
• Subjects: Impact analysis; Machine learning; Parameter sensitivity; Pattern recognition; Performance prediction; Robust classification; Robustness; Sensitivity analysis; Sobol sensitivity analysis; Support vector machines; Uncertainty; Support vector machines; Sobol sensitivity analysis; Uncertainty; Robust classification
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115691

•A new approach is proposed to evaluate the impact of feature uncertainties on SVM.•Sobol analysis is applied to quantify of the impact of each feature uncertainties on SVM.•Feature weights based on Sobol indices are introduced to improve the SVM robustness. This paper addresses the problem of classification when target data are subject to feature uncertainties. A robust approach based on Sobol sensitivity analysis is proposed to improve the robustness of support vector machine (SVM) models. SVM is a supervised machine learning method for pattern recognition whose performance depends on the definition of its hyperparameters and the quality of data. The proposed approach analyzes the impact of the uncertainties on the predictive performance of SVM based on Sobol’ sensitivity analysis. Afterwards, a new parameter is introduced to improve the robustness of SVM to the impact of uncertainties. The efficiency of this approach is evaluated by applying it to six real-world datasets. The results are then discussed and analyzed.