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ACC comparison of different classifiers on KDDTrain+.

NIAID Data Ecosystem2026-03-14 收录

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分类器性能评估

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https://figshare.com/articles/dataset/ACC_comparison_of_different_classifiers_on_KDDTrain_/21712353 数据链接链接失效反馈

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ACC comparison of different classifiers on KDDTrain+.

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创建时间：

2022-12-12

相关数据集

A subset from CSE-CIC-IDS2018

网络入侵检测

恶意流量分类

All data were randomly selected from the CSE-CIC-IDS2018 dataset. The data fields were censored after going through the analysis and 64 valid features were retained.There are 5 types of data, the

DataCite Commons2023-03-31 更新60

Performance comparison of supervised multi-class classifiers on internal and external testing sets.

分类器性能评估

监督与半监督学习模型评估

Class-weighted values are reported.

Figshare2020-03-10 更新40

5-fold cross-validation performance and statistical comparison between CatBoost and Logistic regression models.

机器学习模型比较

分类器性能评估

5-fold cross-validation performance and statistical comparison between CatBoost and Logistic regression models.

NIAID Data Ecosystem20

Additional file 2: of The parameter sensitivity of random forests

随机森林参数调优

分类器性能评估

AUC results for low p/n data. Low p/n results for prediction accuracy using AUC as the performance metric for non-cross-validation results, 10-fold cross-validation and stratified 10-fold cross-valida

NIAID Data Ecosystem60

Statistical significance of linear Vs non-linear classification.

意识状态评估

分类器性能评估

Statistical significance of differences in performance of the different classifiers at loss and recovery of consciousness (LOC and ROC respectively). Classifiers: linear (SVML) and nonlinear (SVMNL) S

Figshare2015-12-02 更新30