Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410959 | Neurocomputing | 2011 | 4 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Sang-Hoon Oh,