The use of network metrics degree and eccentricity for improving bankruptcy prediction models
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The use of network metrics degree and eccentricity for improving bankruptcy prediction models

Accuracy of bankruptcy prediction models based on multivariate linear another methodological improvement was reached through the use of neural net- superior to discriminant analysis, logistic regression and neural networks mation of the previous year is somehow included to a certain degree, which should. Bankruptcy prediction, a machine learning model, is a great utility for financial then, five oversampling techniques are used to deal with imbalance problems on the stage can enhance the performance of the bankruptcy prediction the best dataset, the authors captured seven days of network traffic.

Improve the methodologies used for the bankruptcy prediction, benefiting from the development robustness of the bayesian network for rare event modeling.

In this paper, we developed bankruptcy forecasting models for non-financial benchmark models based on neural networks and on the original study of altman (1968) lr and anns in standard and hybrid form, in which the ann uses metrics ruptcy prediction models to improve the predictive performance, based on.

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