An unified intelligent inference framework for complex modeling and classification

In this paper, an unified three-dimensional inference framework is proposed for modeling and pattern classification under the complex environment where both stochastic and fuzzy uncertainties exist. Based on a three-dimensional probabilistic fuzzy set, this novel inference method integrates the probabilistic inference and fuzzy inference into one operation to improve the computational efficiency and achieve a better performance than that of the traditional fuzzy method or the probabilistic method. The experiments on the wind speed data and Pima Indians Diabetes data demonstrate the advantages and effectiveness of the unified inference framework under the complex stochastic environment.