Description: EasyEnsemble and BalanceCascade are two class-imbalance learning methods. They can adaptively exploit the majority class examples, avoiding important majority class examples to be ignored by common under-sampling while maintaining the fast training speed of under-sampling.
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File list (Check if you may need any files):
EasyEnsemble\BalanceCascade\AdaBoost.m
............\..............\AdjustFPRate.m
............\..............\BalanceCascade.m
............\..............\boost_data.m
............\..............\CalculateAUC.m
............\..............\CalculatePositives.m
............\..............\EvaluateValue.m
............\..............\example_BalanceCascade.m
............\..............\haberman.mat
............\..............\ImbalanceEvaluate.m
............\..............\Predict.m
............\BalanceCascade
............\EasyEnsemble\AdaBoost.m
............\............\boost_data.m
............\............\CalculateAUC.m
............\............\CalculatePositives.m
............\............\EasyEnsemble.m
............\............\EvaluateValue.m
............\............\example_EasyEnsemble.m
............\............\haberman.mat
............\............\ImbalanceEvaluate.m
............\EasyEnsemble
............\EasyEnsemble.htm
EasyEnsemble
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