Neural networks for classification: a survey

Authors:
G. P. Zhang
Affiliations:
Coll. of Bus., Georgia State Univ., Atlanta, GA
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Year:
2000

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Abstract

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