A multi objective approach to evolving artificial neural networks for coronary heart disease classification
Authors: Shenfield, A. and Rostami, S.
Journal: 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015
ISBN: 9781479969265
DOI: 10.1109/CIBCB.2015.7300294
Abstract: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.
https://eprints.bournemouth.ac.uk/24503/
Source: Scopus
A multi objective approach to evolving artificial neural networks for coronary heart disease classification
Authors: Shenfield, A., Rostami, S. and IEEE
Journal: 2015 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB)
Pages: 435-442
https://eprints.bournemouth.ac.uk/24503/
Source: Web of Science (Lite)
A Multi objective Approach to Evolving Artificial Neural Networks for Coronary Heart Disease Classification
Authors: Shenfield, A. and Rostami, S.
Conference: Computational Intelligence in Bioinformatics and Computational Biology
Dates: 12-15 August 2015
Journal: Conference on Computational Intelligence in Bioinformatics and Computational Biology
https://eprints.bournemouth.ac.uk/24503/
Source: Manual
A Multi objective Approach to Evolving Artificial Neural Networks for Coronary Heart Disease Classification
Authors: Shenfield, A. and Rostami, S.
Conference: Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Publisher: IEEE
ISBN: 9781479969265
Abstract: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 optimisa- tion 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
https://eprints.bournemouth.ac.uk/24503/
Source: BURO EPrints