A multi objective approach to evolving artificial neural networks for coronary heart disease classification

Authors: Shenfield, A. and Rostami, S.

http://eprints.bournemouth.ac.uk/24503/

Start date: 12 August 2015

Journal: Conference on Computational Intelligence in Bioinformatics and Computational Biology

This data was imported from Scopus:

Authors: Shenfield, A. and Rostami, S.

http://eprints.bournemouth.ac.uk/24503/

Journal: 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015

ISBN: 9781479969265

DOI: 10.1109/CIBCB.2015.7300294

© 2015 IEEE. The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisation of weights in artificial neural networks (e.g. the Widrow-Hoff least mean squares algorithm and back propagation techniques), there do not exist any hard and fast rules for choosing the structure of an artificial neural network - in particular for choosing both the number of the hidden layers used in the network and the size (in terms of number of neurons) of those hidden layers. However, this internal structure is one of the key factors in determining the accuracy of the classification. This paper proposes taking a multi-objective approach to the evolutionary design of artificial neural networks using a powerful optimiser based around the state-of-the-art MOEA/D-DRA algorithm and a novel method of incorporating decision maker preferences. In contrast to previous approaches, the novel approach outlined in this paper allows the intuitive consideration of trade-offs between classification objectives that are frequently present in complex classification problems but are often ignored. The effectiveness of the proposed multi-objective approach to evolving artificial neural networks is then shown on a real-world medical classification problem frequently used to benchmark classification methods.

This source preferred by Shahin Rostami

This data was imported from Web of Science (Lite):

Authors: Shenfield, A., Rostami, S. and IEEE

http://eprints.bournemouth.ac.uk/24503/

Journal: 2015 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB)

Pages: 435-442

The data on this page was last updated at 04:38 on September 19, 2017.