SIMPOR

This repo implements the idea in the manuscript SIMPOR, a imbalanced learning approache which generates synthetic samples for minority that maximizing posterior ratio.

(The paper is under review and code is under development. Please stay tuned. )

• Set up the environment using pip with the requirment in 'environment.txt'

• Run each dataset separately.
  python main.py --dataset dataset_name -n_runs 2

Error: ImportError: /lib64/libstdc++.so.6: version `CXXABI_1.3.9' not found

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.conda/envs/tensorflow2/lib/

re-run particular experiements

Set CONTINUE to True (main.py)

find and remove exps before re-run

find . -type f -name "SIMPOR.csv" -exec rm -f {} ; find . -type f -name "FinalResult.csv" -exec rm -f {} ;