Error back-propagation algorithm for classification of imbalanced data

Classification of imbalanced data is pervasive but it is a difficult problem to solve. In order to improve the classification of imbalanced data, this letter proposes a new error function for the error back-propagation algorithm of multilayer perceptrons. The error function intensifies weight-updati...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 74; no. 6; pp. 1058 - 1061
Main Author Oh, Sang-Hoon
Format Journal Article
LanguageEnglish
Published Elsevier B.V 15.02.2011
Subjects
Error back-propagation
Error function
Imbalanced data
Imbalanced data
Error back-propagation
Error function
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Summary:Classification of imbalanced data is pervasive but it is a difficult problem to solve. In order to improve the classification of imbalanced data, this letter proposes a new error function for the error back-propagation algorithm of multilayer perceptrons. The error function intensifies weight-updating for the minority class and weakens weight-updating for the majority class. We verify the effectiveness of the proposed method through simulations on mammography and thyroid data sets.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2010.11.024