Abstract
This paper describes two alternatives for hybridizing general type-2 fuzzy logic with the Support Vector Machine (SVM), which is one of the best classification methods in the literature. The main idea of using type-2 fuzzy logic is providing SVM with the ability for uncertainty handling in real-world situations, which suffer from dynamic changes and multiple sources of uncertainty. Two approaches for general type-2 fuzzy hybrid classifiers are proposed, tested and compared based on benchmark data sets. In order to find the best hybrid combination of these methods a comparison has been realized with different experiments using diagnosis benchmark datasets by measuring the classifier accuracy. The first approach consists on using fuzzy rules as additional features to the SVM in order to increase the separability of the data. On the other hand, the second approach consists on defining the Sugeno coefficients for a general type-2 fuzzy classifier as elements of the optimal hyperplane obtained by the SVM method. The motivation for proposing these hybrid approaches is finding the best classifier combining the abilities of the original methods, which are robustness and uncertainty handling. The conclusion based on the experimental results is that the hybrid combination of both methods produces a classifier that is better than the original individual approaches.
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Ontiveros, E., Melin, P. & Castillo, O. Designing hybrid classifiers based on general type-2 fuzzy logic and support vector machines. Soft Comput 24, 18009–18019 (2020). https://doi.org/10.1007/s00500-020-05052-x
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DOI: https://doi.org/10.1007/s00500-020-05052-x