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Conceptual Modeling of Coincident Failures in Multiversion Software
IEEE Transactions on Software Engineering
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Neural networks and the bias/variance dilemma
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Information Sciences: an International Journal
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ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
Expert Systems with Applications: An International Journal
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
A survey of multiple classifier systems as hybrid systems
Information Fusion
Sharpened graph ensemble for semi-supervised learning
Intelligent Data Analysis
Boosted Pre-loaded Mixture of Experts for low-resolution face recognition
International Journal of Hybrid Intelligent Systems
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An appropriate use of neural computing techniques is to apply them to problems such as condition monitoring, fault diagnosis, control and sensing, where conventional solutions can be hard to obtain. However, when neural computing techniques are used, it is important that they are employed so as to maximise their performance, and improve their reliability. Their performance is typically assessed in terms of their ability to generalise to a previously unseen test set, although unless the training set is very carefully chosen, 100% accuracy is rarely achieved. Improved performance can result when sets of neural nets are combined in ensembles and ensembles can be viewed as an example of the reliability through redundancy approach that is recommended for conventional software and hardware in safety-critical or safety-related applications. Although there has been recent interest in the use of neural net ensembles, such techniques have yet to be applied to the tasks of condition monitoring and fault diagnosis. In this paper, we focus on the benefits of techniques which promote diversity amongst the members of an ensemble, such that there is a minimum number of coincident failures. The concept of ensemble diversity is considered in some detail, and a hierarchy of four levels of diversity is presented. This hierarchy is then used in the description of the application of ensemble-based techniques to the case study of fault diagnosis of a diesel engine.