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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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 74; H. 6; S. 1058 - 1061
1. Verfasser:	Oh, Sang-Hoon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 15.02.2011
Schlagworte:	Error back-propagation Error function Imbalanced data Imbalanced data Error back-propagation Error function
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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Zusammenfassung:	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