Neural networks for classification: a survey

Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the l...

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Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part C, Applications and reviews Vol. 30; no. 4; pp. 451 - 462
Main Author Zhang, G.P.
Format Journal Article
LanguageEnglish
Published IEEE 01.11.2000
Subjects
Classification
Classifiers
Costs
Cybernetics
Decision making
Humans
Input variables
Learning
Links
Medical diagnosis
Medical diagnostic imaging
Network synthesis
Neural networks
Probability
Speech recognition
Synthesis
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Summary:Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes 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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1094-6977
1558-2442
DOI:10.1109/5326.897072