title: Robust support vector machine-trained fuzzy system.
abstract: Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.
authors: Department of Computer Engineering, Ferdowsi University of Mashhad, Iran; Department of Computer Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran. Electronic address: yahyafor2000@yahoo.com.
authors: Yahya
authors: Y
authors: Forghani
authors: Department of Computer Engineering, Ferdowsi University of Mashhad, Iran; Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran. Electronic address: h-sadoghi@um.ac.ir.
authors: Hadi Sadoghi
authors: HS
authors: Yazdi
mesh_terms: D000465:Algorithms; D017143:Fuzzy Logic; D006801:Humans; D007858:Learning; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D060388:Support Vector Machine; D013997:Time Factors
keywords: Classification; Fuzzy system; Robust SVM; Rule