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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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Updated
Jan 30, 2024 - Jupyter Notebook
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