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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Expert systems with applications Jg. 189; S. 115691
Hauptverfasser: Zouhri, Wahb, Homri, Lazhar, Dantan, Jean-Yves
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.03.2022
Elsevier BV
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115691