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Multi-objective optimization of base classifiers in StackingC by NSGA-II for intrusion detection

Milliken, Michael, Bi, Yaxin, Galway, Leo and Hawe, Glenn (2017) Multi-objective optimization of base classifiers in StackingC by NSGA-II for intrusion detection. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece. IEEE. 8 pp. [Conference contribution]

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DOI: 10.1109/SSCI.2016.7849977


Multiple Classifier Systems are often found to be useful for improving individual results by combining a set of classifier decisions where a single base level classifier may not achieve the same level of results. However not every set of base classifiers improve results, therefore a selection of a set of classifiers is required. The process of selecting base level classifiers for a multiple classifier system may be performed by the use of a Genetic Algorithm. The aim of this work is the selection of optimal sets of base level classifies using an evolutionary computation approach. In addition, a comparative analysis is made of the performance of the generated ensembles against the individual base level classifiers.

Item Type:Conference contribution (Paper)
Keywords:intrusion detection, cybersecurity, network security
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:36954
Deposited By: Dr Yaxin Bi
Deposited On:22 Feb 2017 15:06
Last Modified:17 Oct 2017 16:27

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