In high-stakes risk prediction, quantifying uncertainty through interval-valued predictions is essential for reliable decision-making. However, standard evaluation tools like the receiver operating characteristic (ROC) curve and the area under the curve (AUC) are designed for point scores and fail to capture the impact of predictive uncertainty on ranking performance. We propose an uncertainty-aware ROC framework specifically for interval-valued predictions, introducing two new measures: $AUC_L$ and $AUC_U$. This framework enables an informative three-region decomposition of the ROC plane, partitioning pairwise rankings into correct, incorrect, and uncertain orderings. This approach naturally supports selective prediction by allowing models to abstain from ranking cases with overlapping intervals, thereby optimizing the trade-off between abstention rate and discriminative reliability. We prove that under valid class-conditional coverage, $AUC_L$ and $AUC_U$ provide formal lower and upper bounds on the theoretical optimal AUC ($AUC^*$), characterizing the physical limit of achievable discrimination. The proposed framework applies broadly to interval-valued prediction models, regardless of the interval construction method. Experiments on real-world benchmark datasets, using bootstrap-based intervals as one instantiation, validate the framework's correctness and demonstrate its practical utility for uncertainty-aware evaluation and decision-making.
Deep Copula Classifier: Theory, Consistency, and Empirical Evaluation
Department of Mathematics, University of Louisiana at Lafayette（路易斯安那大学拉福莱分校数学系）；Department of Statistics, Formerly of Presidency College, Kolkata, India（印度加尔各答 Presidency College 统计学系）
Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is a popular evaluation metric for binary classifiers. In this paper, we discuss techniques to segment the AUC-ROC along human-interpretable dimensions. AUC-ROC is not an additive/linear function over the data samples, therefore such segmenting the overall AUC-ROC is different from tabulating the AUC-ROC of data segments. To segment the overall AUC-ROC, we must first solve an \emph{attribution} problem to identify credit for individual examples. We observe that AUC-ROC, though non-linear over examples, is linear over \emph{pairs}…
On Fixing the Right Problems in Predictive Analytics: AUC Is Not the Problem
University of Pennsylvania（宾夕法尼亚大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Denver（丹佛大学）；Teachers College Columbia University（哥伦比亚大学教育学院）
University of Toronto（多伦多大学）；The Hospital for Sick Children（病童医院）；Lunenfeld-Tanenbaum Research Institute, Sinai Health System（西奈健康系统 Lunenfeld-Tanenbaum 研究所）；Sunnybrook Research Institute（新宁布鲁克研究所）
Receiver operating characteristic (ROC) curve is an informative tool in binary classification and Area Under ROC Curve (AUC) is a popular metric for reporting performance of binary classifiers. In this paper, first we present a comprehensive review of ROC curve and AUC metric. Next, we propose a modified version of AUC that takes confidence of the model into account and at the same time, incorporates AUC into Binary Cross Entropy (BCE) loss used for training a Convolutional neural Network for classification tasks. We demonstrate this on three datasets: MNIST, prostate MRI, and brain MRI. Furth…
Area under ROC (AUC) is an important metric for binary classification and bipartite ranking problems. However, it is difficult to directly optimizing AUC as a learning objective, so most existing algorithms are based on optimizing a surrogate loss to AUC. One significant drawback of these surrogate losses is that they require pairwise comparisons among training data, which leads to slow running time and increasing local storage for online learning. In this work, we describe a new surrogate loss based on a reformulation of the AUC risk, which does not require pairwise comparison but rankings of…
Harvard University（哈佛大学）；Radcliffe Institute for Advanced Study, Harvard University（哈佛大学拉德克利夫高等研究院）；Indian Institute of Science（印度科学院）
The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve, but in terms of the \emph{partial} area under the ROC curve between two false positive rates. In this paper, we develop support vector algorithms for directly optimizing the partial AUC between any two false positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. …
Receiver Operating Characteristic (ROC) curves are plots of true positive rate versus false positive rate which are useful for evaluating binary classification models, but difficult to use for learning since the Area Under the Curve (AUC) is non-convex. ROC curves can also be used in other problems that have false positive and true positive rates such as changepoint detection. We show that in this more general context, the ROC curve can have loops, points with highly sub-optimal error rates, and AUC greater than one. This observation motivates a new optimization objective: rather than maximizi…
Measuring the performance of a classifier is a vital task in machine learning. The running time of an algorithm that computes the measure plays a very small role in an offline setting, for example, when the classifier is being developed by a researcher. However, the running time becomes more crucial if our goal is to monitor the performance of a classifier over time. In this paper we study three algorithms for maintaining two measures. The first algorithm maintains area under the ROC curve (AUC) under addition and deletion of data points in $O(\log n)$ time. This is done by maintaining the dat…
Ottawa Hospital Research Institute（渥太华医院研究所）；Institute of Clinical Evaluative Sciences（临床评估科学研究所）；Department of Family Medicine（家庭医学系）；School of Epidemiology and Public Health（流行病学与公共卫生学院）；University of Ottawa（渥太华大学）；Bruyère Research Institute（布鲁耶尔研究所）；Department of Systems Design Engineering（系统设计工程系）；Vision and Image Processing Lab（视觉与图像处理实验室）；Faculty of Engineering（工程学院）；University of Waterloo（滑铁卢大学）；Fondazione Bruno Kessler Research Institute（布鲁诺·凯斯勒基金会研究所）；Department of Psychology and Cognitive Science（心理学与认知科学系）；University of Trento（特伦托大学）；Department of Cardiology（心脏病学系）；Paracelsus Medical University of Salzburg（萨尔茨堡巴拉塞尔苏斯医科大学）；e-health group（电子健康小组）
Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary classifiers, diagnostic tests or prognosis at a timepoint, measures such as the area under the receiver operating characteristic curve, or the area under the precision recall curve, are too general because they include unrealistic decision thresholds. On the other hand, measures such as accuracy, sensitivity or the F1 score are measures at a single threshold that reflect an individual single probability or predicted ri…
The area under the ROC curve (AUC) is a measure of interest in various machine learning and data mining applications. It has been widely used to evaluate classification performance on heavily imbalanced data. The kernelized AUC maximization machines have established a superior generalization ability compared to linear AUC machines because of their capability in modeling the complex nonlinear structure underlying most real-world data. However, the high training complexity renders the kernelized AUC machines infeasible for large-scale data. In this paper, we present two nonlinear AUC maximizatio…
When dealing with sensitive data in automated data-driven decision-making, an important concern is to learn predictors with high performance towards a class label, whilst minimising for the discrimination towards any sensitive attribute, like gender or race, induced from biased data. A few hybrid tree optimisation criteria exist that combine classification performance and fairness. Although the threshold-free ROC-AUC is the standard for measuring traditional classification model performance, current fair tree classification methods mainly optimise for a fixed threshold on both the classificati…
Scuola Normale Superiore（意大利高等研究院）；University of Pisa（比萨大学）
Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The app…
The University of Iowa（爱荷华大学）；Texas A&M University（得克萨斯农工大学）
The area under the ROC curve (AUC) is one of the most widely used performance measures for classification models in machine learning. However, it summarizes the true positive rates (TPRs) over all false positive rates (FPRs) in the ROC space, which may include the FPRs with no practical relevance in some applications. The partial AUC, as a generalization of the AUC, summarizes only the TPRs over a specific range of the FPRs and is thus a more suitable performance measure in many real-world situations. Although partial AUC optimization in a range of FPRs had been studied, existing algorithms ar…
Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study
School of Computer Science and Information Technology, Adelaide University（阿德莱德大学计算机科学与信息学院）；Australian Institute for Machine Learning (AIML)（澳大利亚机器学习研究所）；Pi MedTech（Pi医疗科技）；School of Electrical and Electronic Engineering, Adelaide University（阿德莱德大学电子与电气工程学院）
Machine Learning-Based Prediction of Dengue Infection Using Clinical and Hematological Data from Bangladesh: A Comparative Analysis of Benchmark and Extended Prediction Models
School of Engineering, Computer and Mathematical Sciences（工程、计算机与数学学院）；Auckland University of Technology (AUT)（奥克兰理工大学）
Fraud detection is a challenging task due to the changing nature of fraud patterns over time and the limited availability of fraud examples to learn such sophisticated patterns. Thus, fraud detection with the aid of smart versions of machine learning (ML) tools is essential to assure safety. Fraud detection is a primary ML classification task; however, the optimum performance of the corresponding ML tool relies on the usage of the best hyperparameter values. Moreover, classification under imbalanced classes is quite challenging as it causes poor performance in minority classes, which most ML c…
We examine the applicability of modern neural network architectures to the midterm prediction of earthquakes. Our data-based classification model aims to predict if an earthquake with the magnitude above a threshold takes place at a given area of size $10 \times 10$ kilometers in $10$-$60$ days from a given moment. Our deep neural network model has a recurrent part (LSTM) that accounts for time dependencies between earthquakes and a convolutional part that accounts for spatial dependencies. Obtained results show that neural networks-based models beat baseline feature-based models that also acc…
University of Iowa（爱荷华大学）；Texas A&M University（得克萨斯农工大学）
As machine learning being used increasingly in making high-stakes decisions, an arising challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected population. A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints, which achieves Pareto efficiency when trading off performance against fairness. Among various fairness metrics, the ones based on the area under the ROC curve (AUC) are emerging recently because they are threshold-agnostic and effective for unbalanced…
Region-of-Interest Augmentation for Mammography Classification under Patient-Level Cross-Validation
Department of Computer Science University of Texas at Arlington（计算机科学系德克萨斯大学阿灵顿分校）；Department of Civil Engineering University of Texas at Arlington（土木工程系德克萨斯大学阿灵顿分校）；Department of Computer Science Colorado State University（计算机科学系科罗拉多州立大学）
Toyota Technological Institute at Chicago（芝加哥丰田技术研究院）；University of Chicago（芝加哥大学）
Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This manuscript presents Deep Convolutional Neural Fields (DeepCNF), a combination of DCNN with Conditional Random Field (CRF), for sequence labeling with highly imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on highly imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Unde…
Aragón Institute for Engineering Research (I3A), University of Zaragoza（萨拉戈萨大学阿拉贡工程研究所（I3A））
Area under the ROC curve (AUC) optimisation techniques developed for neural networks have recently demonstrated their capabilities in different audio and speech related tasks. However, due to its intrinsic nature, AUC optimisation has focused only on binary tasks so far. In this paper, we introduce an extension to the AUC optimisation framework so that it can be easily applied to an arbitrary number of classes, aiming to overcome the issues derived from training data limitations in deep learning solutions. Building upon the multiclass definitions of the AUC metric found in the literature, we d…
In this extended abstract, we will present and discuss opportunities and challenges brought about by a new deep learning method by AUC maximization (aka \underline{\bf D}eep \underline{\bf A}UC \underline{\bf M}aximization or {\bf DAM}) for medical image classification. Since AUC (aka area under ROC curve) is a standard performance measure for medical image classification, hence directly optimizing AUC could achieve a better performance for learning a deep neural network than minimizing a traditional loss function (e.g., cross-entropy loss). Recently, there emerges a trend of using deep AUC ma…
Institut Mines-Télécom（矿业电信学院）；Télécom ParisTech（巴黎电信学院）；CNRS LTCI（法国国家科学研究中心电信与信息处理实验室）；Université Lille 3（里尔第三大学）
In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate rankings exist in the literature, here we focus on the Area Under the ROC Curve (AUC) as it widely used and has a strong theoretical underpinning. In practical recommendation, only items at the top of the ranked list are presented to the users. With this in mind, we propose a class of objective functions over matrix factorisations which primarily represent a smooth surrogate for the real AUC, and in a special case w…
When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
CVI$^2$（CVI²实验室）；SnT, University of Luxembourg（SnT，卢森堡大学）；Cristal Laboratory, National School of Computer Sciences, University of Manouba（Cristal实验室，马努巴国家计算机科学学院）
LTCI, Télécom Paris, Institut Polytechnique de Paris（LTCI、巴黎电信学院、巴黎综合理工学院）；INRIA（法国国家信息与自动化研究所）
Many applications of AI involve scoring individuals using a learned function of their attributes. These predictive risk scores are then used to take decisions based on whether the score exceeds a certain threshold, which may vary depending on the context. The level of delegation granted to such systems in critical applications like credit lending and medical diagnosis will heavily depend on how questions of fairness can be answered. In this paper, we study fairness for the problem of learning scoring functions from binary labeled data, a classic learning task known as bipartite ranking. We arg…
NTT Communication Science Laboratories（NTT通信科学实验室）
We propose a method for maximizing a partial area under a receiver operating characteristic (ROC) curve (pAUC) for binary classification tasks. In binary classification tasks, accuracy is the most commonly used as a measure of classifier performance. In some applications such as anomaly detection and diagnostic testing, accuracy is not an appropriate measure since prior probabilties are often greatly biased. Although in such cases the pAUC has been utilized as a performance measure, few methods have been proposed for directly maximizing the pAUC. This optimization is achieved by using a scorin…
Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulations
School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能信息处理重点实验室）；ByteDance Inc.（字节跳动公司）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）
