A novel multi-stage ensemble model with voting-based outlier adaptation and balanced sampling for credit scoring
收藏NIAID Data Ecosystem2026-03-11 收录
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https://figshare.com/articles/dataset/A_novel_multi-stage_ensemble_model_with_voting-based_outlier_adaptation_and_balanced_sampling_for_credit_scoring/12145842
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资源简介：
Three datasets from
the UC Irvine (UCI) machine learning repository, that is, the
Australian, German, and Japanese datasets, were adopted for the current study. The Australian credit dataset contains 690
samples, of which 307 are
positive and 383 are negative. The dimensions of its input features are 15. The
German credit dataset contains 1000 samples, 700 of which are positive and 300
are negative. The dimensions of its input features are 21. The Japanese credit
dataset contains 690 samples, of
which 383 are positive and 307 are negative. The dimensions of its input
features are 16.
创建时间：
2020-04-17
