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Accuracy of multiple classifiers with a deep-learning-based feature set

Figshare2025-05-27 更新2026-04-28 收录

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https://figshare.com/articles/dataset/Accuracy_of_multiple_classifiers_with_a_deep-learning-based_feature_set/29160604 数据链接链接失效反馈

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Accuracy of multiple classifiers with a deep-learning-based feature set

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创建时间：

2025-05-27

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

Class-specific producer’s accuracies (PA), user’s accuracies (UA), and overall accuracies (OA) (%) for the different classifiers (Manas, training sample number = 4,000).

图像分类

分类器性能评估

Class-specific producer’s accuracies (PA), user’s accuracies (UA), and overall accuracies (OA) (%) for the different classifiers (Manas, training sample number = 4,000).

NIAID Data Ecosystem60

MOESM1 of Improved metagenomic analysis with Kraken 2

元基因组分析

分类器性能评估

Additional file 1: Table S1. Comparison of accuracy and computational performance. Table S2. Comparison of Kraken 2 with other classifiers, using various parameter values. Table S3. Genomes excluded i

DataCite Commons2020-08-26 更新50

The Cost-Effectiveness of Reclassification Sampling for Prevalence Estimation

流行病学抽样方法

分类器性能评估

BackgroundTypically, a two-phase (double) sampling strategy is employed when classifications are subject to error and there is a gold standard (perfect) classifier available. Two-phase sampling involv

NIAID Data Ecosystem60

Leave-one-out cross-validation results.

分类器性能评估

自学习分类器

Recall, precision, and f-measure are calculated for each class. Weighted f-measure, fW, and average accuracy, Acc, are calculated for all classes of a problem. All assessments are based on leave-one-o

NIAID Data Ecosystem50

Accuracy of multiple classifiers with a deep-learning-based feature set

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