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Handling the impact of feature uncertainties on SVM: A robust approach based on Sobol sensitivity analysis
- Source :
- Expert Systems with Applications. 189:115691
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- 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.
- Subjects :
- Computer Science::Machine Learning
Hyperparameter
business.industry
Computer science
General Engineering
Sobol sequence
Machine learning
computer.software_genre
Computer Science Applications
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Artificial Intelligence
Robustness (computer science)
Pattern recognition (psychology)
Feature (machine learning)
Artificial intelligence
Sensitivity (control systems)
business
computer
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 189
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........339b90b61eb2f4fe0d22dd253a0952c9