Python code repository for Enhanced Geometric SMOTE [EG-SMOTE]

Description

EG-SMOTE algorithm is an enhancement of G-SMOTE algorithm for efficient resampling that addresses the limitations of

• Synthesizing noisy minority samples
• Overfitting due to extreme synthesis of minority samples
• Improper synthesis along the borderlines.

Two types of imbalances are handled in EG-SMOTE;

1. between-class imbalance
   • imbalance is handled with improved resampling methodology
2. within-class imbalance
   • rectified by applying the k-means clustering algorithm and then applying resampling within clusters.

Requirement Modules

• Python 3
• numpy
• pandas
• imblearn
• Scikit-learn

Getting Started

• A sample dataset can be found in the Experiment/data/NSLKDD-mini.csv
• The model architecture can be found in egsmote.py.
• And you can try the demo experiment by running Experiment/demo.py file.