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An Efficient Framework for Automatic Algorithm Selection using Meta-Learning

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dc.contributor.advisor Grewal, Gary
dc.contributor.advisor Areibi, Shawki ELmahgiubi, Mohammed 2016-09-08T13:06:58Z 2016-09-08T13:06:58Z 2016-08 2016-08-31 2016-09-08
dc.description.abstract With the unprecedented growth of Information and communication technology (ICT) industry, core networking devices become highly stringent elements of the network due to the increase of packet classification (PC) requirements. Although many PC algorithms with variable performances and capabilities are available, no single algorithm is guaranteed to outperform every other one in every case. This research provides a generic and efficient framework for algorithm selection using Meta-Learning and Artificial Neural Networks (ANN). The developed framework was tested in different scenarios comprising different PC algorithms with different performance measures. Using ANN as the learning model and 10-fold cross validation as the evaluation criteria, the framework was able to achieve an average accuracy of 92.5% on predicting the most suitable algorithm that maximizes classification speed for an unseen ruleset, and 88% when minimizing memory footprint on a larger set of algorithms using the same evaluation criteria. en_US
dc.language.iso en en_US
dc.subject Machine Learning en_US
dc.subject Meta-Learning en_US
dc.subject Algorithm Selection en_US
dc.subject Packet Classification en_US
dc.title An Efficient Framework for Automatic Algorithm Selection using Meta-Learning en_US
dc.type Thesis en_US Engineering en_US Master of Applied Science en_US School of Engineering en_US
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