Credit Index Screening Model of Family Farms and Family Ranches Based on Fuzzy Bayesian Theory of Depth Weighting

Abstract

The construction of credit evaluation index system of Chinese family farm and pasture is not only a theoretical problem but also of great practical significance. In this paper, based on the depth-weighted Bayesian theory and fuzzy mathematics, the improved depth-weighted fuzzy Bayesian hybrid algorithm model is proposed to solve the unbalanced problem of default status of family farm and pasture and to build the index system with the ability of three categories of default identification. In this paper, the characteristics of the first one is based on fuzzy set theory, the definition of fuzzy linguistic assessment of different default set, family ranches characteristic is converted to the corresponding index of pasting with triangular fuzzy mathematical model, and then through the inner method converting triangular fuzzy number into accurate output data to deal with the blur and uncertainty about the state of the fuzzy default transformation is realized. Second, based on the insensitive characteristic of ROC curve to skewness samples, the depth weighting of characteristic indexes in nondefault, low default and high-default states was completed by constructing multiclassification ROC curve, which solved the practical problem of sample imbalance in different default states of family farms and ranches, and selected the index system with significant discrimination ability for default states by integrating default identification ability.