A novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions

SHENFIELD, Alex, RODRIGUES, Marcos, MORENO-CUESTA, Jeronimo and NOORELDEEN, Hossam (2017). A novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions. In: CIBCB 2017 : IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Manchester, 23-25 August 2017.

Preview

PDF cibcb-2017-mortality.pdf - Submitted Version All rights reserved. Download (350kB) | Preview

Abstract

The optimisation of classifier performance in pattern recognition and medical prognosis tasks is a complex and poorly understood problem. Classifier performance is greatly affected by the choice of artificial neural network architecture and starting weights and biases - yet there exists very little guidance in the literature as to how to choose these parameters. Recently evolutionary artificial neural networks have been proposed to mitigate some of these problems; however, whilst evolutionary methods are extremely effective in finding global optima, they are notoriously computationally expensive (often requiring tens of thousands of function evaluations to arrive at a solution). This paper proposes a novel hybrid adaptive approach to the optimisation of artificial neural network parameters where the global search capabilities of differential evolution and the efficiency of local search heuristics (such as resilient back-propagation for artificial neural network training) are combined. A state-of-the-art adaptive differential evolution algorithm, JADE, has been chosen as the basis for this hybrid algorithm due to its proven effectiveness in optimising high dimensional problems. The performance of this hybrid adaptive differential evolution algorithm is then demonstrated in the design of a classifier for mortality risk prediction in a critical care environment, where the optimised classifier is shown to outperform the current state-of-the-art in risk prediction.

Item Type:	Conference or Workshop Item (Paper)
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Depositing User:	Alex Shenfield
Date Deposited:	24 Jul 2017 12:41
Last Modified:	18 Mar 2021 16:22
URI:	https://shura.shu.ac.uk/id/eprint/16215

Actions (login required)

View Item

Downloads

Publication Metrics