cuhksz-ning/balanced-evolutionary-semi-stacking

Balanced-Evolutionary-Semi-Stacking

• Code for paper Ning, Zhihan, et al. "BESS: Balanced evolutionary semi-stacking for disease detection using partially labeled imbalanced data." Information Sciences 594 (2022): 233-248.

• Required Python 3 packages:

  1. sklearn (https://github.com/scikit-learn/scikit-learn)
  2. imblearn (https://github.com/scikit-learn-contrib/imbalanced-learn)
  3. lightgbm (optional, https://github.com/microsoft/LightGBM)

• BESS is compatible with most sklearn APIs but is not strictly tested.

• Run: python3 main.py

• Import: from BalancedEvolutionarySemiStacking import BalancedEvolutionarySemiStacking

• Train: fit(X, y), with target -1 as the unlabeled data, 0 as the majority class, and 1 as the minority class.

• Predict: predict(X) or predict_proba(X)