Using symbolic and connectionist algorithms to knowledge acquisition for credit evaluation

Author

Horst, P.S. ; Padilha, T.P.P. ; Rocha, C.A.J. ; Rezende, S.O. ; Carvalho, A.C.P.L.

Author_Institution

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

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

277

Abstract

There are several techniques of artificial intelligence being applied on the financial market, including credit evaluation. This work investigates the performance achieved by different artificial intelligence techniques when applied to credit evaluation. The techniques used were MLP neural networks and two symbolic learning algorithms, CN2 and C4.5. In order to analyze the performance obtained by these techniques, two distinct data sets for credit evaluation were used. The knowledge used by these techniques was also compared to the knowledge extracted from trained neural networks using a knowledge extraction tool

Keywords

credit transactions; financial data processing; knowledge acquisition; multilayer perceptrons; symbol manipulation; AI; C4.5; CN2; MLP neural networks; artificial intelligence; connectionist algorithms; credit evaluation; data sets; financial market; knowledge acquisition; knowledge extraction; symbolic algorithms; symbolic learning algorithms; Artificial intelligence; Artificial neural networks; Computational intelligence; Computer science; Credit cards; Data mining; Knowledge acquisition; Laboratories; Machine learning algorithms; Statistics;

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.1998.682277

Filename

682277