Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis
•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 class...
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| Vydáno v: | Expert systems with applications Ročník 189; s. 115691 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Elsevier Ltd
01.03.2022
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •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. |
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| AbstractList | •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. 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. |
| ArticleNumber | 115691 |
| Author | Homri, Lazhar Zouhri, Wahb Dantan, Jean-Yves |
| Author_xml | – sequence: 1 givenname: Wahb surname: Zouhri fullname: Zouhri, Wahb email: wahb.zouhri@ensam.eu – sequence: 2 givenname: Lazhar surname: Homri fullname: Homri, Lazhar email: lazhar.homri@ensam.eu – sequence: 3 givenname: Jean-Yves surname: Dantan fullname: Dantan, Jean-Yves email: jean-yves.dantan@ensam.eu |
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| Snippet | •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... This paper addresses the problem of classification when target data are subject to feature uncertainties. A robust approach based on Sobol sensitivity analysis... |
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| SubjectTerms | Impact analysis Machine learning Parameter sensitivity Pattern recognition Performance prediction Robust classification Robustness Sensitivity analysis Sobol sensitivity analysis Support vector machines Uncertainty |
| Title | Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis |
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