Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance

SHENFIELD, Alex and ROSTAMI, Shahin (2017). Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance. In: CIBCB 2017 : IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Manchester, 23-25 August 2017. [Conference or Workshop Item]

Documents
16213:197905
[thumbnail of cibcb2017_fetal_ctg.pdf]
Preview
PDF
cibcb2017_fetal_ctg.pdf - Submitted Version
Available under License All rights reserved.

Download (335kB) | Preview
Abstract
This paper proposes a novel multi-objective optimisation approach to solving both the problem of finding good structural and parametric choices in an ANN and the problem of training a classifier with a heavily skewed data set. The state-of-the-art CMA-PAES-HAGA multi-objective evolutionary algorithm is used to simultaneously optimise the structure, weights, and biases of a population of ANNs with respect to not only the overall classification accuracy, but the classification accuracies of each individual target class. The effectiveness of this approach is then demonstrated on a real-world multi-class problem in medical diagnosis (classification of fetal cardiotocogorams) where more than 75% of the data belongs to the majority class and the rest to two other minority classes. The optimised ANN is shown to significantly outperform a standard feed-forward ANN with respect to minority class recognition at the cost of slightly worse performance in terms of overall classification accuracy.
More Information
Statistics

Downloads

Downloads per month over past year

Share
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Actions (login required)

View Item View Item