An Intuitionistic Fuzzy Random Vector Functional Link Classifier

Random vector functional link (RVFL) is a widely used powerful model for solving real-life problems in classification and regression. However, the RVFL is not able to reduce the impact of noisy data, despite its high generalization capability. This paper presents a new intuitionistic RVFL classifier (IFRVFLC) for binary classification with the goals of improving the overall classification capability of the RVFL network and increasing its classification efficiency on noisy data sets. In IFRVFLC, each training sample is associated with an intuitionistic fuzzy number which consists of membership or non-membership frames. The membership degree of a pattern considers the distance from the respective class centre. The degree of non-membership, on the other hand, is determined by the ratio of the heterogeneous point number to the total number of neighbouring points. To check the efficiency of the proposed IFRVFLC model, its classification performance is compared with the support vector machine (SVM), twin SVM, kernel ridge regression, extreme learning machine , intuitionistic fuzzy SVM, intuitionistic fuzzy twin SVM and RVFL networks. The obtained results show the usability of the proposed IFRVFLC model.