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Bankruptcy prediction using connectionist and symbolic learning algorithms

descriptionPublicationkeyboard_double_arrow_right Article 27 Nov 2002Publisher:IEEEJournal:1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), volume 1, pages 271-276

Authors: A.C.P.L.F. de Carvalho; Helio Diniz; Solange Oliveira Rezende; E. Martineli;

doi: 10.1109/ijcnn.1998.682276

Bankruptcy prediction using connectionist and symbolic learning algorithms

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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.

Related Organizations

Universidade de São Paulo
Brazil
University of São Paulo
Brazil

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