198 lines
19 KiB
TeX
198 lines
19 KiB
TeX
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\section{Data Sets}\label{sec:experiments:data_sets}
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\addcontentsline{tocheb}{section}{\protect\numberline{\secnumforhebrewtoc}{מערך נתונים}}
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\paragraph{KEEL benchmark.}
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We evaluate on 12 binary imbalanced datasets from the KEEL repository~\cite{alcalafdez2011keel}. All labels are clean. \Cref{tab:datasets:keel} summarises the datasets; imbalance ratios range from 1.8 to 11.6. No known noise is reported in the original data.
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\begin{table}[htbp]
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\centering
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\caption{KEEL benchmark datasets used in experiments. IR = negative/positive count ratio.}
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\label{tab:datasets:keel}
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\small
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\begin{tabular}{lrrrrl}
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\toprule
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Dataset & $n$ & Pos & Neg & IR & Source \\
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\midrule
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Glass0 & 214 & 70 & 144 & 2.1 & Glass identification (UCI) \\
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Glass1 & 214 & 76 & 138 & 1.8 & Glass identification (UCI) \\
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Glass2 & 214 & 17 & 197 & 11.6 & Glass identification (UCI) \\
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Glass6 & 214 & 29 & 185 & 6.4 & Glass identification (UCI) \\
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Haberman & 306 & 81 & 225 & 2.8 & Breast cancer survival, 1958--1970 \\
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Iris0 & 150 & 50 & 100 & 2.0 & \emph{Iris setosa} vs.\ others \\
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New-Thyroid1 & 215 & 35 & 180 & 5.1 & Thyroid function classification \\
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Pima & 768 & 268 & 500 & 1.9 & Pima Indian diabetes (UCI) \\
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Vehicle2 & 846 & 218 & 628 & 2.9 & Vehicle silhouette (UCI) \\
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Vowel0 & 988 & 90 & 898 & 10.0 & Vowel recognition, class 0 vs.\ rest \\
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Wisconsin & 683 & 239 & 444 & 1.9 & Wisconsin breast cancer (UCI) \\
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Yeast3 & 1484 & 163 &1321 & 8.1 & Yeast protein localisation (UCI) \\
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\bottomrule
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\end{tabular}
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\end{table}
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\paragraph{CIFAR-10N.}
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CIFAR-10 is a widely used image dataset of 60{,}000 32x32 color images belonging to 10 mutually exclusive classes. CIFAR-10N~\cite{wei2022cifar10n} augments the 50{,}000 training set labels with real human-annotated noisy labels collected from Amazon Mechanical Turk~\cite{wei2022cifar10n}. We form a binary task (class~1, automobile, vs.\ rest; IR~$\approx 9$:1) and train under three noise conditions:
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\emph{clean} (original CIFAR-10 labels, 0\% noise),
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\emph{aggre} (majority-vote aggregate of crowdworker annotations, $\approx$9\% noise), and
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\emph{worse} (single worst-annotator labels, $\approx$40\% noise).
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This allows us to measure the degradation of the model's performance under increasing label noise conditions.
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The architecture tested is a three-block convolutional network trained from scratch.
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\paragraph{Large datasets.}
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We include four large-scale datasets spanning diverse imbalanced real-world settings.
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\begin{itemize}
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\item \textbf{Bank Marketing}~\cite{moro2014bank}, UCI repository; Portuguese bank telephone marketing campaigns (Moro et al., 2014). $n = 41{,}188$ samples, 48 features after one-hot encoding. Positive class: client subscribed to term deposit. IR~$\approx 8$:1 ($\approx$11.3\% positive). No label noise.
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\item \textbf{Credit Card Fraud}~\cite{dalpozzolo2015calibrating}, Kaggle / ULB (Worldline + MLG, 2013). $n = 284{,}807$ transactions, 30 features (V1--V28 are PCA-transformed; Time and Amount rescaled). Extreme imbalance: 492 confirmed frauds, IR~$\approx 577$:1. Labels reflect confirmed-fraud reports; undetected fraudulent transactions constitute a structural source of class-conditional false negatives~\cite{tittelfitz2023fpr}.
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\item \textbf{Home Credit}~\cite{homecredit2018kaggle}, Kaggle Home Credit Default Risk competition (2018). $n = 307{,}511$ loan applications, $\approx$120 numerical features from the primary application table. Positive class: loan default. IR~$\approx 11.4$:1 ($\approx$8\% default rate). Labels are proxy labels (repayment outcome); no injected noise.
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\item \textbf{NIH ChestX-ray14}~\cite{wang2017chestxray14}, a large-scale medical chest radiograph dataset of $112{,}120$ X-rays from $30{,}805$ patients annotated with 14 pathology labels. Labels were extracted from radiological reports via NLP; the dataset authors estimate accuracy at $>$90\%~\cite{wang2017chestxray14}, though subsequent audits have found the true error rate substantially higher~\cite{oakdenrayner2020exploring}, making this a naturally noisy benchmark. We form a binary task targeting \emph{pleural effusion} detection (Effusion vs.\ rest; IR~$\approx 7.5$:1). Unlike the other three datasets, inputs here are not raw tabular features: we extract $1{,}024$-dimensional latent representations using a \mbox{DenseNet-121} pretrained on the dataset via the TorchXRayVision library~\cite{cohen2022torchxrayvision}, with global average pooling applied to the penultimate feature map, and pass these frozen embeddings to a lightweight $1024{\to}512{\to}128{\to}1$ MLP with dropout. This transfer-learning setup lets us evaluate ROLL without GPU-intensive end-to-end training.
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\end{itemize}
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\section{Performance Measures}\label{sec:experiments:performance_measures}
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\addcontentsline{tocheb}{section}{\protect\numberline{\secnumforhebrewtoc}{מדדי ביצועים}}
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We report the \emph{area under the ROC curve} (AUC) averaged over independent training episodes (see \Cref{sec:experiments:configurations}). AUC measures overall ranking quality and is a standard metric for imbalanced classification. All results are presented as mean $\pm$ standard deviation over episodes.
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\section{Configurations and Parameter Settings}
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\label{sec:experiments:configurations}
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\addcontentsline{tocheb}{section}{\protect\numberline{\secnumforhebrewtoc}{קונפיגורציה ופרמטרים}}
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We compare \textsc{Roll}-AOC (the primary proposed loss, optimising the area under the ROC curve via KDE) against the following baselines:
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\begin{itemize}
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\item \textbf{BCE-W}: binary cross-entropy with class-frequency inverse weighting.
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\item \textbf{Focal}: focal loss~\cite{lin2017focal} with focusing parameter $\gamma = 2$.
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\item \textbf{Asym.}: asymmetric loss~\cite{zhou2021asymmetric}.
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\item \textbf{GCE}: generalised cross-entropy~\cite{zhang2018generalized} with $q = 0.7$.
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\item \textbf{LibAUC}: PESG-based AUC maximisation from the LibAUC library~\cite{yuan2021auc}.
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\item \textbf{MAE}: mean absolute error (symmetric noise-robust loss).
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\end{itemize}
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All methods use the same network architecture, optimiser (Adam for non-LibAUC methods), and early stopping. The best model per-episode is chosen from the epoch with lowest loss. Each configuration is run for $n = 5$ independent episodes on KEEL and large datasets, and $n = 3$ on CIFAR-10N. Reported AUC is evaluated on the test split, averaged between episodes.
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\section{Results}\label{sec:experiments:results}
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\addcontentsline{tocheb}{section}{\protect\numberline{\secnumforhebrewtoc}{תוצאות}}
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\subsection{KEEL Benchmark}\label{sec:results:keel}
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\Cref{tab:results:keel} reports mean test AUC across the 12 KEEL datasets under clean labels.
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No single method dominates: on easy datasets (Iris0, Wisconsin, Vowel0, Vehicle2, Glass6) nearly all methods converge to similar AUCs, leaving little room for differentiation.
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On the harder, more imbalanced datasets the picture is more varied.
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\textsc{Roll}-AOC competes well but does not claim the top position on most individual datasets, establishing it as a competitive approach.
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To assess label-noise robustness, we inject symmetric label noise. A fraction of all class labels are randomly flipped.
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We evaluate at noise levels of $5\%, 10\%, 20\%$, running 10 independent episodes per dataset per noise level.
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The randomly flipped labels change each episode, yet are consistent between configurations, ensuring every configuration, per episode, sees the same labels, changed in the same way.
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Full per-dataset results at 5\% and 10\% noise are given in \Cref{appendix:keel-noise} (\Cref{tab:results:keel-noise-05,tab:results:keel-noise-10}); the 20\% results, where differentiation is strongest, are shown in \Cref{tab:results:keel-noise-20} below.
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At $0\%$ noise, results are quite similar across configurations. \textsc{Roll}-AOC trails right behind the leader in most configurations. Since datasets are small, the \textbf{std} given for each point is relatively high, and quite a bit of variability is expected. Still, with few exceptions, results between configurations are within throwing distance of each other.
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As noise increases the ranking shifts markedly: at $20\%$ noise \textsc{Roll}-AOC reaches the top on average (0.847), while traditionally noise-robust losses --- \textsc{GCE} (0.831) and \textsc{MAE} (0.804) --- degrade substantially.
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The per-dataset results in \Cref{tab:results:keel-noise-20} reveal that this trend is consistent across most datasets rather than driven by a small subset: \textsc{Roll}-AOC leads on 6 of 12 datasets (Glass0, Glass1, Glass2, Pima, Vehicle2, Yeast3), while no other method leads on more than 3.
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From $0\%$ to $20\%$ noise, \textsc{Roll}-AOC drops 0.033 in mean AUC (3.7\%), compared to 0.056 for \textsc{GCE} (6.3\%), 0.071 for \textsc{MAE} (8.1\%), and 0.073 for \textsc{LibAUC} (8.3\%).
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This supports the hypothesis that ROLL's distributional objective provides implicit noise robustness: mislabelled samples shift the fitted score distribution gradually rather than producing isolated large-gradient outliers.
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\begin{table}[htbp]
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\centering
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\caption{Mean test AUC ($\pm$\,std, $n=5$ episodes) on KEEL benchmark datasets under clean labels. Best per row in \textbf{bold}.}
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\label{tab:results:keel}
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\resizebox{\textwidth}{!}{%
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\begin{tabular}{lccccccc}
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\toprule
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Dataset & \textsc{Roll}-AOC & BCE-W & Focal & Asym. & GCE & LibAUC & MAE \\
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\midrule
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Glass0 & 0.796{\tiny{$\pm$0.046}} & 0.839{\tiny{$\pm$0.033}} & 0.802{\tiny{$\pm$0.020}} & 0.814{\tiny{$\pm$0.034}} & 0.818{\tiny{$\pm$0.032}} & \textbf{0.843{\tiny{$\pm$0.035}}} & 0.826{\tiny{$\pm$0.037}} \\
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Glass1 & 0.722{\tiny{$\pm$0.033}} & 0.710{\tiny{$\pm$0.018}} & 0.712{\tiny{$\pm$0.036}} & 0.744{\tiny{$\pm$0.044}} & 0.742{\tiny{$\pm$0.039}} & 0.723{\tiny{$\pm$0.054}} & \textbf{0.769{\tiny{$\pm$0.031}}} \\
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Glass2 & 0.723{\tiny{$\pm$0.062}} & 0.661{\tiny{$\pm$0.069}} & 0.677{\tiny{$\pm$0.072}} & \textbf{0.750{\tiny{$\pm$0.075}}} & 0.689{\tiny{$\pm$0.023}} & 0.719{\tiny{$\pm$0.084}} & 0.679{\tiny{$\pm$0.018}} \\
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Glass6 & 0.975{\tiny{$\pm$0.014}} & 0.972{\tiny{$\pm$0.025}} & 0.974{\tiny{$\pm$0.020}} & 0.982{\tiny{$\pm$0.013}} & 0.971{\tiny{$\pm$0.020}} & 0.969{\tiny{$\pm$0.012}} & \textbf{0.985{\tiny{$\pm$0.006}}} \\
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Haberman & 0.696{\tiny{$\pm$0.045}} & 0.736{\tiny{$\pm$0.012}} & \textbf{0.742{\tiny{$\pm$0.025}}} & 0.741{\tiny{$\pm$0.018}} & 0.737{\tiny{$\pm$0.016}} & 0.616{\tiny{$\pm$0.116}} & 0.671{\tiny{$\pm$0.102}} \\
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Iris0 & \textbf{1.000{\tiny{$\pm$0.000}}} & 0.900{\tiny{$\pm$0.224}} & \textbf{1.000{\tiny{$\pm$0.000}}} & \textbf{1.000{\tiny{$\pm$0.000}}} & \textbf{1.000{\tiny{$\pm$0.000}}} & \textbf{1.000{\tiny{$\pm$0.000}}} & 0.900{\tiny{$\pm$0.224}} \\
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New-Thyroid1 & 0.989{\tiny{$\pm$0.010}} & 0.992{\tiny{$\pm$0.005}} & 0.977{\tiny{$\pm$0.033}} & 0.988{\tiny{$\pm$0.007}} & 0.987{\tiny{$\pm$0.011}} & 0.985{\tiny{$\pm$0.012}} & \textbf{0.994{\tiny{$\pm$0.003}}} \\
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Pima & 0.778{\tiny{$\pm$0.024}} & \textbf{0.787{\tiny{$\pm$0.014}}} & 0.775{\tiny{$\pm$0.020}} & 0.771{\tiny{$\pm$0.019}} & 0.785{\tiny{$\pm$0.005}} & 0.761{\tiny{$\pm$0.015}} & 0.778{\tiny{$\pm$0.005}} \\
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Vehicle2 & 0.982{\tiny{$\pm$0.003}} & \textbf{0.984{\tiny{$\pm$0.004}}} & 0.982{\tiny{$\pm$0.003}} & 0.980{\tiny{$\pm$0.004}} & 0.982{\tiny{$\pm$0.004}} & 0.982{\tiny{$\pm$0.003}} & 0.983{\tiny{$\pm$0.004}} \\
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Vowel0 & 0.993{\tiny{$\pm$0.005}} & 0.995{\tiny{$\pm$0.004}} & 0.996{\tiny{$\pm$0.003}} & 0.995{\tiny{$\pm$0.003}} & 0.997{\tiny{$\pm$0.003}} & 0.991{\tiny{$\pm$0.007}} & \textbf{0.998{\tiny{$\pm$0.001}}} \\
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Wisconsin & 0.997{\tiny{$\pm$0.002}} & \textbf{0.999{\tiny{$\pm$0.000}}} & \textbf{0.999{\tiny{$\pm$0.001}}} & 0.999{\tiny{$\pm$0.001}} & 0.999{\tiny{$\pm$0.001}} & 0.999{\tiny{$\pm$0.000}} & 0.998{\tiny{$\pm$0.001}} \\
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Yeast3 & 0.906{\tiny{$\pm$0.024}} & 0.894{\tiny{$\pm$0.008}} & 0.908{\tiny{$\pm$0.019}} & \textbf{0.935{\tiny{$\pm$0.014}}} & 0.927{\tiny{$\pm$0.006}} & \textbf{0.935{\tiny{$\pm$0.016}}} & 0.915{\tiny{$\pm$0.010}} \\
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\bottomrule
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\end{tabular}%
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}
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\end{table}
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\begin{table}[htbp]
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\centering
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\caption{Mean test AUC ($\pm$\,std, $n=10$ episodes) on KEEL datasets at $\epsilon=20\%$ asymmetric label noise. Best per row in \textbf{bold}.}
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\label{tab:results:keel-noise-20}
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\resizebox{\textwidth}{!}{%
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\begin{tabular}{lccccccc}
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\toprule
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Dataset & \textsc{Roll}-AOC & BCE-W & Focal & Asym. & GCE & LibAUC & MAE \\
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\midrule
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Glass0 & \textbf{0.801{\tiny{$\pm$0.042}}} & 0.767{\tiny{$\pm$0.042}} & 0.796{\tiny{$\pm$0.040}} & 0.793{\tiny{$\pm$0.051}} & 0.765{\tiny{$\pm$0.052}} & 0.745{\tiny{$\pm$0.089}} & 0.791{\tiny{$\pm$0.044}} \\
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Glass1 & \textbf{0.725{\tiny{$\pm$0.036}}} & 0.718{\tiny{$\pm$0.054}} & 0.714{\tiny{$\pm$0.036}} & 0.700{\tiny{$\pm$0.039}} & 0.706{\tiny{$\pm$0.041}} & 0.635{\tiny{$\pm$0.052}} & 0.689{\tiny{$\pm$0.060}} \\
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Glass2 & \textbf{0.622{\tiny{$\pm$0.110}}} & 0.620{\tiny{$\pm$0.166}} & 0.620{\tiny{$\pm$0.082}} & 0.586{\tiny{$\pm$0.121}} & 0.614{\tiny{$\pm$0.196}} & 0.601{\tiny{$\pm$0.138}} & 0.573{\tiny{$\pm$0.163}} \\
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Glass6 & 0.915{\tiny{$\pm$0.077}} & 0.925{\tiny{$\pm$0.063}} & 0.939{\tiny{$\pm$0.057}} & \textbf{0.962{\tiny{$\pm$0.025}}} & 0.875{\tiny{$\pm$0.102}} & 0.939{\tiny{$\pm$0.046}} & 0.827{\tiny{$\pm$0.150}} \\
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Haberman & 0.632{\tiny{$\pm$0.099}} & 0.601{\tiny{$\pm$0.129}} & 0.662{\tiny{$\pm$0.091}} & 0.633{\tiny{$\pm$0.110}} & 0.586{\tiny{$\pm$0.131}} & \textbf{0.666{\tiny{$\pm$0.130}}} & 0.629{\tiny{$\pm$0.106}} \\
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Iris0 & 0.993{\tiny{$\pm$0.011}} & 0.997{\tiny{$\pm$0.009}} & 0.897{\tiny{$\pm$0.217}} & 0.950{\tiny{$\pm$0.158}} & 0.957{\tiny{$\pm$0.135}} & 0.998{\tiny{$\pm$0.004}} & \textbf{1.000{\tiny{$\pm$0.001}}} \\
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New-Thyroid1 & 0.930{\tiny{$\pm$0.047}} & 0.951{\tiny{$\pm$0.044}} & 0.962{\tiny{$\pm$0.035}} & 0.895{\tiny{$\pm$0.160}} & \textbf{0.973{\tiny{$\pm$0.065}}} & 0.888{\tiny{$\pm$0.300}} & 0.910{\tiny{$\pm$0.152}} \\
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Pima & \textbf{0.758{\tiny{$\pm$0.015}}} & 0.756{\tiny{$\pm$0.024}} & 0.753{\tiny{$\pm$0.032}} & 0.742{\tiny{$\pm$0.048}} & 0.757{\tiny{$\pm$0.026}} & 0.741{\tiny{$\pm$0.027}} & 0.745{\tiny{$\pm$0.027}} \\
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Vehicle2 & \textbf{0.949{\tiny{$\pm$0.024}}} & 0.948{\tiny{$\pm$0.021}} & 0.936{\tiny{$\pm$0.026}} & 0.928{\tiny{$\pm$0.029}} & 0.932{\tiny{$\pm$0.038}} & 0.864{\tiny{$\pm$0.049}} & 0.892{\tiny{$\pm$0.057}} \\
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Vowel0 & 0.966{\tiny{$\pm$0.033}} & \textbf{0.978{\tiny{$\pm$0.019}}} & 0.975{\tiny{$\pm$0.018}} & 0.966{\tiny{$\pm$0.015}} & 0.956{\tiny{$\pm$0.020}} & 0.847{\tiny{$\pm$0.157}} & 0.838{\tiny{$\pm$0.079}} \\
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Wisconsin & 0.995{\tiny{$\pm$0.003}} & \textbf{0.998{\tiny{$\pm$0.002}}} & 0.997{\tiny{$\pm$0.002}} & 0.995{\tiny{$\pm$0.006}} & 0.996{\tiny{$\pm$0.004}} & 0.993{\tiny{$\pm$0.014}} & 0.997{\tiny{$\pm$0.002}} \\
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Yeast3 & \textbf{0.879{\tiny{$\pm$0.032}}} & 0.861{\tiny{$\pm$0.070}} & 0.853{\tiny{$\pm$0.063}} & 0.846{\tiny{$\pm$0.091}} & 0.850{\tiny{$\pm$0.038}} & 0.793{\tiny{$\pm$0.104}} & 0.752{\tiny{$\pm$0.133}} \\
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\bottomrule
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\end{tabular}%
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}
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\end{table}
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\subsection{CIFAR-10N: Label-Noise Robustness}\label{sec:results:cifar10n}
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\Cref{tab:results:cifar10n} shows AUC under three noise levels on CIFAR-10N. Under \emph{clean} labels all methods reach AUC $\geq 0.972$, with \textsc{Roll}-AOC at the top (0.980). As noise increases the advantage of \textsc{Roll}-AOC grows: under \emph{aggre} noise it leads by 0.002 over the next-best method (GCE, 0.932) while other methods degrade more significantly, and under \emph{worse} noise, the margin widens to 0.012 over MAE (0.809) and 0.047 over BCE-W (0.738). Observing degradation relative to the clean scenario, \textsc{Roll}-AOC degraded the least ($-0.159$ relative to clean results), with MAE second ($-0.167$).
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\begin{table}[htbp]
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\centering
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\caption{Mean test AUC ($\pm$\,std, $n=3$ episodes) on CIFAR-10N under three label-noise levels. Best per row in \textbf{bold}.}
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\label{tab:results:cifar10n}
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\resizebox{\textwidth}{!}{%
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\begin{tabular}{lccccccc}
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\toprule
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Noise & \textsc{Roll}-AOC & BCE-W & Focal & Asym. & GCE & LibAUC & MAE \\
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\midrule
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Clean & \textbf{0.980{\tiny{$\pm$0.001}}} & 0.977{\tiny{$\pm$0.002}} & 0.977{\tiny{$\pm$0.000}} & 0.972{\tiny{$\pm$0.003}} & 0.976{\tiny{$\pm$0.002}} & 0.979{\tiny{$\pm$0.000}} & 0.976{\tiny{$\pm$0.002}} \\
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Aggre. & \textbf{0.934{\tiny{$\pm$0.002}}} & 0.924{\tiny{$\pm$0.003}} & 0.912{\tiny{$\pm$0.014}} & 0.910{\tiny{$\pm$0.004}} & 0.932{\tiny{$\pm$0.005}} & 0.921{\tiny{$\pm$0.004}} & 0.930{\tiny{$\pm$0.002}} \\
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Worse & \textbf{0.821{\tiny{$\pm$0.001}}} & 0.738{\tiny{$\pm$0.009}} & 0.734{\tiny{$\pm$0.009}} & 0.720{\tiny{$\pm$0.004}} & 0.774{\tiny{$\pm$0.005}} & 0.752{\tiny{$\pm$0.006}} & 0.809{\tiny{$\pm$0.004}} \\
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\bottomrule
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\end{tabular}%
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}
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\end{table}
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\subsection{Large Datasets}\label{sec:results:large}
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\Cref{tab:results:large} covers the four large-scale datasets. On Bank Marketing, LibAUC leads (0.936) with \textsc{Roll}-AOC close behind (0.931), and both substantially outperform all other baselines. On Credit Card Fraud \textsc{Roll}-AOC is the top performer (0.968), outperforming all baselines. On Home Credit \textsc{Roll}-AOC edges LibAUC (0.712 vs.\ 0.711).
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NIH ChestX-ray14 is qualitatively distinct from the other three: inputs are frozen DenseNet-121 embeddings from radiograph images rather than raw tabular features, making this a transfer-learning evaluation under naturally noisy labels~\cite{oakdenrayner2020exploring}.
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\textsc{Roll}-AOC achieves the highest AUC ($0.839\pm0.001$), a clear margin above the next-best method GCE ($0.829\pm0.004$).
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Standard cross-entropy variants trail substantially: BCE-W reaches $0.788\pm0.008$, Focal $0.798\pm0.009$, and Asym.\ $0.729\pm0.007$. MAE ($0.810\pm0.001$) and LibAUC ($0.835\pm0.002$) both fall short despite their respective noise-robust and AUC-optimising designs.
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The ${\approx}5$~pp gap between \textsc{Roll}-AOC and BCE-W, consistent with the controlled KEEL noise experiment (\Cref{sec:results:keel}) and CIFAR-10N (\Cref{sec:results:cifar10n}), confirms that the noise-robustness advantage holds on real-world label noise. The result also shows that \textsc{Roll}-AOC generalises beyond from-scratch training: with embeddings frozen, ROLL operates entirely within the MLP head, fitting score distributions over a fixed representation.
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\begin{table}[htbp]
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\centering
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\caption{Mean test AUC ($\pm$\,std, $n=5$ episodes) on large real-world datasets. Best per row in \textbf{bold}. ChestX-ray14 uses frozen DenseNet-121 embeddings rather than raw tabular features.}
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\label{tab:results:large}
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\resizebox{\textwidth}{!}{%
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\begin{tabular}{lccccccc}
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\toprule
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Dataset & \textsc{Roll}-AOC & BCE-W & Focal & Asym. & GCE & LibAUC & MAE \\
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\midrule
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Bank Marketing & 0.931{\tiny{$\pm$0.002}} & 0.580{\tiny{$\pm$0.040}} & 0.573{\tiny{$\pm$0.044}} & 0.684{\tiny{$\pm$0.033}} & 0.746{\tiny{$\pm$0.022}} & \textbf{0.936{\tiny{$\pm$0.003}}} & 0.707{\tiny{$\pm$0.035}} \\
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Credit Card Fraud & \textbf{0.968{\tiny{$\pm$0.001}}} & 0.949{\tiny{$\pm$0.015}} & 0.942{\tiny{$\pm$0.020}} & 0.940{\tiny{$\pm$0.013}} & 0.948{\tiny{$\pm$0.014}} & 0.966{\tiny{$\pm$0.006}} & 0.954{\tiny{$\pm$0.009}} \\
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Home Credit & \textbf{0.712{\tiny{$\pm$0.001}}} & 0.693{\tiny{$\pm$0.002}} & 0.694{\tiny{$\pm$0.002}} & 0.688{\tiny{$\pm$0.003}} & 0.698{\tiny{$\pm$0.002}} & 0.711{\tiny{$\pm$0.001}} & 0.699{\tiny{$\pm$0.004}} \\
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\midrule
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ChestX-ray14 (Effusion) & \textbf{0.839{\tiny{$\pm$0.001}}} & 0.788{\tiny{$\pm$0.008}} & 0.798{\tiny{$\pm$0.009}} & 0.729{\tiny{$\pm$0.007}} & 0.829{\tiny{$\pm$0.004}} & 0.835{\tiny{$\pm$0.002}} & 0.810{\tiny{$\pm$0.001}} \\
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\bottomrule
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\end{tabular}%
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}
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\end{table}
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