A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms

被引：134

作者：

Lee, KD

Booth, D ^{[1
]}

Alam, P

机构：

[1] Kent State Univ, Coll Business Adm, Dept Management & Informat Syst, Kent, OH 44242 USA

[2] Univ Incheon, Coll Business Adm, Inchon 420749, South Korea

[3] Kent State Univ, Coll Business Adm, Dept Accounting, Kent, OH 44242 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2005年 / 29卷 / 01期

关键词：

supervised network; unsupervised network; bankruptcy prediction; Kohonen self-organizing feature map;

D O I：

10.1016/j.eswa.2005.01.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study, two learning paradigms of neural networks, supervised versus unsupervised, are compared using their representative types. The back-propagation (BP) network and the Kohonen self-organizing feature map, selected as the representative type for supervised and unsupervised neural networks, respectively, are compared in terms of prediction accuracy in the area of bankruptcy prediction. Discriminant analysis and logistic regression are also performed to provide performance benchmarks. The findings suggest that the BP network is a better choice when a target vector is available. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：1 / 16

页数：16

共 47 条

[41] PREDICTING JAPANESE CORPORATE BANKRUPTCY IN TERMS OF FINANCIAL DATA USING NEURAL-NETWORK [J].