A Comparison of Discriminant Analysis versus Artificial Neural Networks
作者:
YoonYoungohc,
SwalesGeorge,
MargavioThomas M.,
期刊:
Journal of the Operational Research Society
(Taylor Available online 1993)
卷期:
Volume 44,
issue 1
页码: 51-60
ISSN:0160-5682
年代: 1993
DOI:10.1057/jors.1993.6
出版商: Taylor&Francis
关键词: Artificial Neural Networks;Discriminant Analysis;Finance;Non-Linear Models;Statistics
数据来源: Taylor
摘要:
AbstractArtificial Neural Network (ANN) techniques have recently been applied to many different fields and have demonstrated their capabilities in solving complex problems. In a business environment, the techniques have been applied to predict bond ratings and stock price performance. In these applications, ANN techniques outperformed widely-used multivariate statistical techniques. The purpose of this paper is to compare the ANN method with the Discriminant Analysis (DA) method in order to understand the merits of ANN that are responsible for the higher level of performance. The paper provides an overview of the basic concepts of ANN techniques in order to enhance the understanding of this emerging technique. The similarities and differences between ANN and DA techniques in representing their models are described. This study also proposes a method to overcome the limitations of the ANN approach, Finally, a case study using a data set in a business environment demonstrates the superiority of ANN over DA as a method of classification of observations.
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