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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.
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Required Python 3 packages:
- sklearn (https://github.com/scikit-learn/scikit-learn)
- imblearn (https://github.com/scikit-learn-contrib/imbalanced-learn)
- lightgbm (optional, https://github.com/microsoft/LightGBM)
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BESS is compatible with most sklearn APIs but is not strictly tested.
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Run:
python3 main.py
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Import:
from BalancedEvolutionarySemiStacking import BalancedEvolutionarySemiStacking
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Train:
fit(X, y)
, with target-1
as the unlabeled data,0
as the majority class, and1
as the minority class. -
Predict:
predict(X)
orpredict_proba(X)
CUHKSZ-NING/Balanced-Evolutionary-Semi-Stacking
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