Twomey, David;
Gorse, Denise;
(2022)
ASTra: A Novel Algorithm-Level Approach to Imbalanced Classification.
In: Pimenidis, Elias and Angelov, Plamen and Jayne, Chrisina and Papaleonidas, Antonios and Aydin, Mehmet, (eds.)
Artificial Neural Networks and Machine Learning – ICANN 2022, Proceedings, Part III.
(pp. pp. 569-580).
Springer Nature Switzerland AG: Cham, Switzerland.
Text
2209.01685.pdf - Accepted Version Access restricted to UCL open access staff until 16 September 2024. Download (347kB) |
Abstract
We propose a novel output layer activation function, which we name ASTra (Asymmetric Sigmoid Transfer function), which makes the classification of minority examples, in scenarios of high imbalance, more tractable. We combine this with a loss function that helps to effectively target minority misclassification. These two methods can be used together or separately, with their combination recommended for the most severely imbalanced cases. The proposed approach is tested on datasets with IRs from 588.24 to 4000 and very few minority examples (in some datasets, as few as five). Results using neural networks with from two to 12 hidden units are demonstrated to be comparable to, or better than, equivalent results obtained in a recent study that deployed a wide range of complex, hybrid data-level ensemble classifiers.
Type: | Proceedings paper |
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Title: | ASTra: A Novel Algorithm-Level Approach to Imbalanced Classification |
Event: | 31st International Conference on Artificial Neural Networks, ICANN |
Location: | Univ W England, Bristol, ENGLAND |
Dates: | 6 Sep 2022 - 9 Sep 2022 |
ISBN-13: | 978-3-031-15933-6 |
DOI: | 10.1007/978-3-031-15934-3_47 |
Publisher version: | https://doi.org/10.1007/978-3-031-15934-3_47 |
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. |
Keywords: | Classification, Class imbalance, Adaptive activation function, Asymmetric sigmoid, Confusion matrix, Geometric mean |
UCL classification: | UCL 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/10172668 |
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