Qualitative company performance evaluation: Linear discriminant analysis and neural network models

被引:25
|
作者
Bertels, K [1 ]
Jacques, JM [1 ]
Neuberg, L [1 ]
Gatot, L [1 ]
机构
[1] Univ Namur, Dept Business Adm, B-5000 Namur, Belgium
关键词
artificial intelligence; gradient methods; multivariate statistics; networks;
D O I
10.1016/S0377-2217(98)00161-1
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
In this paper, we present a classification model to evaluate the performance of companies on the basis of qualitative criteria, such as organizational and managerial variables. The classification model evaluates the eligibility of the company to receive state subsidies for the development of high tech products. We furthermore created a similar model using the backpropagation learning algorithm and compare its classification performance against the linear model. We also focus on the robustness of the two approaches with respect to uncertain information. This research shows that backpropagation neural networks are not superior to LDA-models (Linear Discriminant Analysis), except when they are given highly uncertain information. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:608 / 615
页数:8
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