Выбор системы экономических показателей для диагностики и прогнозирования банкротств на основе нейросетевого байесовского подхода
There are a lot of models of bankruptcies prediction, which differ in methods of modeling and in set of factors. These methods are mainly belong to 5 groups: the classic statistical methods, regression analysis, the method of discriminant analysis, logit-analysis methods, methods of fuzzy sets and neural network methods. Combinations of these methods also can take place. The last three groups of methods are currently being developed especially quickly. As for the choice of factors bankruptcy, prevails heuristics. There is no formal methodology for selection and comparison groups of economic indicators to build the model of bankruptcies, as well as effective methods for data preprocessing. In this paper we propose an original method for the choice of indicators, followed by the construction of neural network model diagnostic of bankruptcies based on Bayesian approach.