Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction

被引：96

作者：

Olmeda I. ^{[1
]}

Fernández E. ^{[2
]}

机构：

[1] Depto. Fundamentos Economia H., Universidad de Alcalá

[2] Depto. de Cie. de la Comp., Universidad de Alcalá

来源：

Computational Economics | 1997年 / 10卷 / 4期

关键词：

Artificial neural networks; Bankruptcy prediction; C4.5; Discriminant analysis; Evolutionary programming; Logit; MARS;

D O I：

10.1023/A:1008668718837

中图分类号：

学科分类号：

摘要：

This paper compares the accuracy of parametric and nonparametric classifiers on the problem of predicting Bankruptcy. Among the single classifiers, an artificial neural network is found to provide the best results. Two ways of combining classifiers are considered and an additive aggregation method is proposed. We show that both ways of combining produce classifiers whose forecasts are more accurate than the ones obtained with any single model. We suggest that an optimal system for risk rating should combine two or more different techniques.

引用

页码：317 / 335

页数：18

共 19 条

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