期刊
NEUROCOMPUTING
卷 74, 期 6, 页码 1058-1061出版社
ELSEVIER
DOI: 10.1016/j.neucom.2010.11.024
关键词
Error back-propagation; Imbalanced data; Error function
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. (C) 2010 Elsevier B.V. All rights reserved.
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