Neural networks for classification: A survey

被引:1037
|
作者
Zhang, GQP [1 ]
机构
[1] Georgia State Univ, Coll Business, Atlanta, GA 30303 USA
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2000年 / 30卷 / 04期
关键词
Bayesian classifier; classification; ensemble methods; feature variable selection; learning and generalization; misclassification costs; neural networks;
D O I
10.1109/5326.897072
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes the some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.
引用
收藏
页码:451 / 462
页数:12
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