Intuitionistic Fuzzy Inference System with Genetic Tuning for Predicting Financial Performance

Abstract

Intuitionistic fuzzy inference systems are used to model the uncertainty associated with positive and negative information and preferences. Here, we propose a novel intuitionistic fuzzy inference system of the Takagi-Sugeno-Kang type with genetic tuning. A genetic fuzzy apriori algorithm is used to obtain both the set of if-then rules and the initial values of the premise parameters. Then, a genetic algorithm is applied to tune the premise and consequent parameters of the intuitionistic fuzzy inference system. We demonstrate the effectiveness of the proposed system for predicting corporate financial performance and show that the system has higher prediction accuracy than state-of-the-art fuzzy inference systems.