Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance
Authors: Shenfield, A. and Rostami, S.
Journal: 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
ISBN: 9781467389884
DOI: 10.1109/CIBCB.2017.8058553
Abstract:This paper proposes a novel multi-objective optimisatìon 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 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 significantiy outperform a standard feed-forward ANN with respect to minority class recognition at the cost of slightiy worse performance in terms of overall classification accuracy.
https://eprints.bournemouth.ac.uk/29999/
Source: Scopus
Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance
Authors: Shenfield, A. and Rostami, S.
Journal: 2017 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB)
Pages: 217-224
https://eprints.bournemouth.ac.uk/29999/
Source: Web of Science (Lite)
Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance
Authors: Shenfield, A. and Rostami, S.
Conference: IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology
Dates: 23 August-25 June 2017
https://eprints.bournemouth.ac.uk/29999/
Source: Manual
Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance
Authors: Shenfield, A. and Rostami, S.
Conference: IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology
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.
https://eprints.bournemouth.ac.uk/29999/
Source: BURO EPrints