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Performance of C4.5 for the whole sample.

NIAID Data Ecosystem2026-03-11 收录

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数据链接：

https://figshare.com/articles/dataset/Performance_of_C4_5_for_the_whole_sample_/8309543 数据链接链接失效反馈

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资源简介：

Performance of C4.5 for the whole sample.

应用场景：

创建时间：

2019-06-21

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Test results for different features.

特征工程评估

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Summary of results for multiple feature types on optimal weighted F scores. (FNR = False Negative Rate, TPR = True Positive Rate, FPR = False Positive Rate, TNR = True Negative Rate).

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DENV-Any classification accuracy for the best vaccine and placebo models for each input variable data set.

登革热疫苗评估

分类模型性能

DENV-Any classification accuracy for the best vaccine and placebo models for each input variable data set.

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: Results of classifications performed without any feature selection, with the proposed method, using only the pre-selection step (Spearman’s correlation analysis) or only the selection by RF algorithm (RF importance coefficients).

特征选择评估

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Optimal classification accuracy with filtered subsets and IFS.

特征优化

分类模型性能

Optimal classification accuracy with filtered subsets and IFS.

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Test set classification accuracies and percentage of label requests per episode.

主动学习

分类模型性能

Test set classification accuracies and percentage of label requests per episode.

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