Papers Ordinal Classification
“Ordinal Classification” 태그가 달린 논문 92편 · 필터 해제
ADABORD: a novel AdaBoost approach for ordinal classification
Ordinal Classification (OC) deals with classification tasks where the classes follow a natural order. Despite the progress in OC, many existing approaches fail to fully leverage the ordinal information, treating the prob…
Ordinal ClassificationA Unified Algebraic Framework for Classification Performance Evaluation
We propose a unified algebraic framework for classification performance evaluation that encompasses binary, multiclass, multilabel, ordinal, hierarchical, cost-sensitive, and soft-label settings within a single formalism…
Ordinal ClassificationReliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score
Ordinal classification (OC) arises in high-stakes domains such as medicine and finance, where uncertainty quantification must account for the severity of ordinal errors. Conformal prediction (CP) provides distribution-fr…
Ordinal ClassificationA novel ordinal multi-view aggregation scheme for oak defoliation
Forest decline driven by climate and biotic stressors threatens ecosystem functioning, making accurate monitoring of tree health essential. In this work, we address tree defoliation estimation as an ordinal classificatio…
Ordinal ClassificationAuditing demographic bias in AI-based emergency police dispatch: a cross-lingual evaluation of eleven large language models
Large language models (LLMs) are rapidly being integrated into high-stakes public safety systems, including emergency call triage and dispatch decision support, yet their demographic fairness in this context remains larg…
Ordinal ClassificationOrdinal Semantic Segmentation Applied to Medical and Odontological Images
Semantic segmentation consists of assigning a semantic label to each pixel according to predefined classes. This process facilitates the understanding of object appearance and spatial relationships, playing an important …
Ordinal ClassificationSemantic SegmentationClassifier Pooling for Modern Ordinal Classification
Ordinal data is widely prevalent in clinical and other domains, yet there is a lack of both modern, machine-learning based methods and publicly available software to address it. In this paper, we present a model-agnostic…
Ordinal ClassificationTornadoNet: Real-Time Building Damage Detection with Ordinal Supervision
We present TornadoNet, a comprehensive benchmark for automated street-level building damage assessment evaluating how modern real-time object detection architectures and ordinal-aware supervision strategies perform under…
Building Damage AssessmentReal-Time Object DetectionOrdinal ClassificationExtreme-value forest fire prediction A study of the Loss Function in an Ordinality Scheme
Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging. In this work, we introduce the first ordinal classification framework for fore…
Ordinal ClassificationProvably Minimum-Length Conformal Prediction Sets for Ordinal Classification
Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential for decision making. Conformal prediction …
Ordinal ClassificationDecision MakingBeyond MSE: Ordinal Cross-Entropy for Probabilistic Time Series Forecasting
Time series forecasting is an important task that involves analyzing temporal dependencies and underlying patterns (such as trends, cyclicality, and seasonality) in historical data to predict future values or trends. Cur…
Probabilistic Time Series ForecastingOrdinal ClassificationValue predictionPredicting Household Water Consumption Using Satellite and Street View Images in Two Indian Cities
Monitoring household water use in rapidly urbanizing regions is hampered by costly, time-intensive enumeration methods and surveys. We investigate whether publicly available imagery-satellite tiles, Google Street View (G…
Ordinal ClassificationOrdinal Label-Distribution Learning with Constrained Asymmetric Priors for Imbalanced Retinal Grading
Diabetic retinopathy grading is inherently ordinal and long-tailed, with minority stages being scarce, heterogeneous, and clinically critical to detect accurately. Conventional methods often rely on isotropic Gaussian pr…
Diabetic Retinopathy GradingOrdinal ClassificationApproximately Unimodal Likelihood Models for Ordinal Regression
Ordinal regression (OR, also called ordinal classification) is classification of ordinal data, in which the underlying target variable is categorical and considered to have a natural ordinal relation for the underlying e…
Ordinal ClassificationATTS: Asynchronous Test-Time Scaling via Conformal Prediction
Large language models (LLMs) benefit from test-time scaling but are often hampered by high inference latency. Speculative decoding is a natural way to accelerate the scaling process; however, scaling along both the paral…
Ordinal ClassificationOrdinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels
Labeled data is a fundamental component in training supervised deep learning models for computer vision tasks. However, the labeling process, especially for ordinal image classification where class boundaries are often a…
Ordinal ClassificationImage ClassificationAge EstimationEZ-Sort: Efficient Pairwise Comparison via Zero-Shot CLIP-Based Pre-Ordering and Human-in-the-Loop Sorting
Pairwise comparison is often favored over absolute rating or ordinal classification in subjective or difficult annotation tasks due to its improved reliability. However, exhaustive comparisons require a massive number of…
Image Quality AssessmentOrdinal ClassificationAge EstimationCLoE: Curriculum Learning on Endoscopic Images for Robust MES Classification
Estimating disease severity from endoscopic images is essential in assessing ulcerative colitis, where the Mayo Endoscopic Subscore (MES) is widely used to grade inflammation. However, MES classification remains challeng…
Ordinal ClassificationTOC-UCO: a comprehensive repository of tabular ordinal classification datasets
An ordinal classification (OC) problem corresponds to a special type of classification characterised by the presence of a natural order relationship among the classes. This type of problem can be found in a number of rea…
Ordinal ClassificationAleatoric and Epistemic Uncertainty Measures for Ordinal Classification through Binary Reduction
Ordinal classification problems, where labels exhibit a natural order, are prevalent in high-stakes fields such as medicine and finance. Accurate uncertainty quantification, including the decomposition into aleatoric (in…
Ordinal ClassificationBayesian Inference