TY  - GEN
PB  - i6doc.com
UR  - https://www.i6doc.com/en/book/?gcoi=28001100176760
ID  - discovery10115865
N2  - We introduce a new cost function for the training of a neural network classifier in conditions of high class imbalance. This function, based on an approximate confusion matrix, represents a balance of sensitivity and specificity and is thus well suited to problems where cost functions such as the mean squared error and cross entropy are prone to overpredicting the majority class. The benefit of the new measure is shown on a set of common class-imbalanced datasets using the Matthews Correlation Coefficient as an independent scoring measure.
A1  - Twomey, D
A1  - Gorse, D
CY  - Bruges, Belgium
EP  - 212
AV  - public
Y1  - 2018/01/01/
SP  - 207
TI  - A neural network cost function for highly class-imbalanced data sets
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -