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Improving the Error Back-Propagation Algorithm for Imbalanced Data Sets
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 Title & Authors
Improving the Error Back-Propagation Algorithm for Imbalanced Data Sets
Oh, Sang-Hoon;
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Imbalanced data sets are difficult to be classified since most classifiers are developed based on the assumption that class distributions are well-balanced. In order to improve the error back-propagation algorithm for the classification of imbalanced data sets, a new error function is proposed. The error function controls weight-updating with regards to the classes in which the training samples are. This has the effect that samples in the minority class have a greater chance to be classified but samples in the majority class have a less chance to be classified. The proposed method is compared with the two-phase, threshold-moving, and target node methods through simulations in a mammography data set and the proposed method attains the best results.
Imbalanced Data;Error Back-Propagation;Error Function;Mammography;
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