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A Multi objective Approach to Evolving Artificial Neural Networks for Coronary Heart Disease Classification.

Shenfield, A. and Rostami, S., 2015. A Multi objective Approach to Evolving Artificial Neural Networks for Coronary Heart Disease Classification. In: Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 12--15 August 2015, IEEE.

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Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1109/CIBCB.2015.7300294


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

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Optimization; Biological neural networks; Heart; Diseases; Sociology; Statistics; Approximation methods
Group:Faculty of Science & Technology
ID Code:24503
Deposited By: Symplectic RT2
Deposited On:31 Aug 2016 15:18
Last Modified:14 Mar 2022 13:57


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