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

Shenfield, A. and Rostami, S., 2017. Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance. In: IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, 23-25 June 2017, Manchester. (In Press)

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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 [41] 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 cardiotocograms) 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.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:29999
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:22 Nov 2017 11:18
Last Modified:22 Nov 2017 11:18

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