AN EXPERIMENTAL EVALUATION OF NEURAL NETWORKS FOR CLASSIFICATION

被引:38
作者
SUBRAMANIAN, V
HUNG, MS
HU, MY
机构
[1] KENT STATE UNIV, COLL BUSINESS ADM, KENT, OH 44242 USA
[2] UNIV WISCONSIN PARKSIDE, SCH BUSINESS, KENOSHA, WI 53141 USA
[3] KENT STATE UNIV, GRAD SCH, KENT, OH 44242 USA
关键词
D O I
10.1016/0305-0548(93)90063-O
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Artificial neural networks are new methods for classification. In this paper, we describe how to build neural network models. These models are then compared with classical models such as linear discriminant analysis and quadratic discriminant analysis. While neural network models can solve some difficult classification problems where classical models cannot, the results show that even under best conditions for the classical models, neural networks are quite competitive. Furthermore, neural networks are more robust in that they are less sensitive to changes in sample size, number of groups, number of variables, proportions of group memberships, and degrees of overlap among groups.
引用
收藏
页码:769 / 782
页数:14
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