Bankruptcy prediction using connectionist and symbolic learning algorithms

Author

Martineli, E. ; Diniz, H. ; de Carvalho, A.C.P.L.F. ; Rezende, S.O.

Author_Institution

Dept. of Comput. Sci., Sao Paulo Univ., Brazil

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

271

Abstract

This article describes the use of connectionist and symbolic learning algorithms in the problem of bankruptcy prediction. Data about Brazilian banks represented by 26 or 10 indicators of their current financial situation were used. The difference among the number of existent examples in the classes of bankrupt and nonbankrupt banks was livened up through the reduction of learning examples of the class of nonbankrupts and the addition of noise samples in the class of bankrupts

Keywords

bank data processing; forecasting theory; learning (artificial intelligence); neural nets; noise; symbol manipulation; Brazilian banks; bankruptcy prediction; connectionist learning algorithms; symbolic learning algorithms; Artificial intelligence; Artificial neural networks; Backpropagation algorithms; Computational intelligence; Computer science; Economic forecasting; Laboratories; Machine learning algorithms; Noise reduction; Production;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.682276

Filename

682276