Twomey, D;
Gorse, D;
(2018)
A neural network cost function for highly class-imbalanced data sets.
In:
ESANN 2018 - Proceedings 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningESANN 2017 - Proceedings 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
(pp. pp. 207-212).
i6doc.com: Bruges, Belgium.
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | A neural network cost function for highly class-imbalanced data sets |
| Event: | ESANN 2018 |
| ISBN-13: | 9782875870476 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.i6doc.com/en/book/?gcoi=28001100176760 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10115865 |
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