ggjay9/Application-Flow-Identification

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.

Language: Jupyter Notebook - Size: 90.6 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

jzlka/namon

Language: C++ - Size: 29.8 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 5 - Forks: 2