Classifying Internet Traffic using An Efficient Classifier
Haitham A.Jamil1, Hind G. Abdelrahim2, Bushra M Ali3, Azza O. Awad4
1SHaitham A. Jamil, University of Elimam Elmahdi, Kosti, White Nile, Sudan.
2Bushra M Ali, University Technology Malaysia, Johor Bahru, Malaysia.
3Hind G. Abdelrahim, University Technology Malaysia, Johor Bahru, Malaysia.
4Azza O. Awad,University Technology Malaysia, Johor Bahru, Malaysia
Manuscript received on 1 August 2019. | Revised Manuscript received on 8 August 2019. | Manuscript published on 30 September 2019. | PP: 577-582 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2041078219/19©BEIESP | DOI: 10.35940/ijrte.B2041.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier performance was measured using accuracy metric. Some experimentation results show that the ensemble methods of pre-trained VGG19-Resnet56 and VGG16-VGG19-Resnet models outperform the accuracy of its individual model and other composite models made of these three models.
Keywords: Ensemble Classifiers, VGG16, VGG19, Resnet56, Probability Voting Technique, CIFAR-10.
Scope of the Article: IoT