Papers Ordinal Classification
“Ordinal Classification” 태그가 달린 논문 92편 · 필터 해제
Ordinal classification for interval-valued data and interval-valued functional data
The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs. Interval-valued data refers to data in the form of intervals. For the first time, interval-valued data and i…
Ordinal ClassificationOrdinal Classification with Distance Regularization for Robust Brain Age Prediction
Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging ref…
Age EstimationOrdinal ClassificationPerformance Metrics for Probabilistic Ordinal Classifiers
Ordinal classification models assign higher penalties to predictions further away from the true class. As a result, they are appropriate for relevant diagnostic tasks like disease progression prediction or medical image …
DiagnosticOrdinal ClassificationLearning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification
We present a novel language-driven ordering alignment method for ordinal classification. The labels in ordinal classification contain additional ordering relations, making them prone to overfitting when relying solely on…
Age EstimationClassificationLearning-To-RankOrdinal ClassificationConvolutional and Deep Learning based techniques for Time Series Ordinal Classification
Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the…
BenchmarkingOrdinal ClassificationTime SeriesTime Series ClassificationA generalized framework to predict continuous scores from medical ordinal labels
Many variables of interest in clinical medicine, like disease severity, are recorded using discrete ordinal categories such as normal/mild/moderate/severe. These labels are used to train and evaluate disease severity pre…
Multi-class ClassificationOrdinal Classificationseverity predictionA Dictionary-based approach to Time Series Ordinal Classification
Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-call…
ClassificationOrdinal ClassificationTime SeriesTime Series ClassificationInterpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images
Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the literature on computer-assisted detecti…
image-classificationImage ClassificationOrdinal ClassificationControlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning
For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes. Intuitively, when Euclidean distance metric is…
Ordinal ClassificationUltra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification
Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problems of efficiency and challenging scenarios like severe occlusions and extreme lighting conditi…
Lane DetectionOrdinal ClassificationQuasi-Unimodal Distributions for Ordinal Classification
Ordinal classification tasks are present in a large number of different domains. However, common losses for deep neural networks, such as cross-entropy, do not properly weight the relative ordering between classes. For t…
ClassificationOrdinal ClassificationImproving the repeatability of deep learning models with Monte Carlo dropout
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Repeatable models output predictions with low variation during…
AttributeBinary ClassificationClassificationDensity Estimation+6Decreasing Annotation Burden of Pairwise Comparisons with Human-in-the-Loop Sorting: Application in Medical Image Artifact Rating
Ranking by pairwise comparisons has shown improved reliability over ordinal classification. However, as the annotations of pairwise comparisons scale quadratically, this becomes less practical when the dataset is large. …
Ordinal ClassificationCSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms. The missed screeni…
BenchmarkingOrdinal ClassificationPrognosisA Novel Machine Learning Approach to Data Inconsistency with respect to a Fuzzy Relation
Inconsistency in prediction problems occurs when instances that relate in a certain way on condition attributes, do not follow the same relation on the decision attribute. For example, in ordinal classification with mono…
AttributeBIG-bench Machine LearningOrdinal ClassificationRelationAutomatic Estimation of Ulcerative Colitis Severity from Endoscopy Videos using Ordinal Multi-Instance Learning
Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by relapsing inflammation of the large intestine. The severity of UC is often represented by the Mayo Endoscopic Subscore (MES) which quantifi…
Ordinal ClassificationReal-Time Multi-Level Neonatal Heart and Lung Sound Quality Assessment for Telehealth Applications
Digital stethoscopes in combination with telehealth allow chest sounds to be easily collected and transmitted for remote monitoring and diagnosis. Chest sounds contain important information about a newborn's cardio-respi…
feature selectionOrdinal ClassificationEvaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification
Ordinal Classification (OC) is an important classification task where the classes are ordinal. For example, an OC task for sentiment analysis could have the following classes: highly positive, positive, neutral, negative…
ClassificationOrdinal ClassificationSentiment AnalysisRecursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation
The unsupervised domain adaptation (UDA) has been widely adopted to alleviate the data scalability issue, while the existing works usually focus on classifying independently discrete labels. However, in many tasks (e.g.,…
Age EstimationDomain AdaptationMedical DiagnosisOrdinal Classification+1A theoretical look at ordinal classification methods based on reference sets composed of characteristic actions
From a theoretical view, this paper addresses the general problem of designing ordinal classification methods based on comparing actions with subset of actions, which are representative of their classes (categories). The…
Ordinal ClassificationRelation