Fractional Intuitionistic Fuzzy Support Vector Machine: Diabetes Tweet Classification

Abstract

Support vector machine (SVM) models apply the Karush–Kuhn–Tucker (KKT-OC) optimality conditions in the ordinary derivative to the primal optimisation problem, which has a major influence on the weights associated with the dissimilarity between the selected support vectors and subsequently on the quality of the model’s predictions. Recognising the capacity of fractional derivatives to provide machine learning models with more memory through more microscopic differentiations, in this paper we generalise KKT-OC based on ordinary derivatives to KKT-OC using fractional derivatives (Frac-KKT-OC). To mitigate the impact of noise and identify support vectors from noise, we apply the Frac-KKT-OC method to the fuzzy intuitionistic version of SVM (IFSVM). The fractional fuzzy intuitionistic SVM model (Frac-IFSVM) is then evaluated on six sets of data from the UCI and used to predict the sentiments embedded in tweets posted by people with diabetes. Taking into account four performance measures (sensitivity, specificity, F-measure, and G-mean), the Frac-IFSVM version outperforms SVM, FSVM, IFSVM, Frac-SVM, and Frac-FSVM.