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application-identification
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Classify applications using flow features with Random Forest and K-Nearest Neighbor classifiers. Explore augmentation techniques like oversampling, SMOTE, BorderlineSMOTE, and ADASYN for better handling of underrepresented classes. Measure classifier effectiveness for different sampling techniques using accuracy, precision, recall, and F1-score.
machine-learning random-forest network-analysis knn sampling-methods application-identification xai-evaluation smote-sampling adasyn-sampling
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Jan 30, 2024 - Jupyter Notebook
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