Papers Ordinal Classification
“Ordinal Classification” 태그가 달린 논문 92편 · 필터 해제
Supervised Contrastive Learning for Ordinal Engagement Measurement
Student engagement plays a crucial role in the successful delivery of educational programs. Automated engagement measurement helps instructors monitor student participation, identify disengagement, and adapt their teachi…
Contrastive LearningData AugmentationOrdinal ClassificationMultimodal Assessment of Classroom Discourse Quality: A Text-Centered Attention-Based Multi-Task Learning Approach
Classroom discourse is an essential vehicle through which teaching and learning take place. Assessing different characteristics of discursive practices and linking them to student learning achievement enhances the unders…
MathMulti-Task LearningOrdinal ClassificationAI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality
Purpose: This study presents a Deep Learning (DL)-based quality assessment (QA) approach for evaluating auto-generated contours (auto-contours) in radiotherapy, with emphasis on Online Adaptive Radiotherapy (OART). Lever…
Decision MakingOrdinal ClassificationTransfer LearningUncertainty QuantificationEnhancing Depression Detection via Question-wise Modality Fusion
Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves determining the depression severity of a person through self-reported qu…
Depression DetectionOrdinal ClassificationTrust Modeling in Counseling Conversations: A Benchmark Study
In mental health counseling, a variety of earlier studies have focused on dialogue modeling. However, most of these studies give limited to no emphasis on the quality of interaction between a patient and a therapist. The…
Ordinal ClassificationCLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss
In ordinal classification, misclassifying neighboring ranks is common, yet the consequences of these errors are not the same.For example, misclassifying benign tumor categories is less consequential, compared to an e…
ClassificationContrastive LearningOrdinal ClassificationGenerative Regression Based Watch Time Prediction for Short-Video Recommendation
Watch time prediction (WTP) has emerged as a pivotal task in short video recommendation systems, designed to quantify user engagement through continuous interaction modeling. Predicting users' watch times on videos often…
Language ModelingLanguage ModellingOrdinal ClassificationPrediction+3From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking
Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such full rankings in practice, most CF models r…
Collaborative FilteringOrdinal ClassificationSplitting criteria for ordinal decision trees: an experimental study
Ordinal Classification (OC) is a machine learning field that addresses classification tasks where the labels exhibit a natural order. Unlike nominal classification, which treats all classes as equally distinct, OC takes …
ClassificationOrdinal ClassificationLAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification
We introduce LAMPO, a novel paradigm that leverages Large Language Models (LLMs) for solving few-shot multi-class ordinal classification tasks. Unlike conventional methods, which concatenate all demonstration examples wi…
Hate Speech DetectionOrdinal Classificationdlordinal: a Python package for deep ordinal classification
dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of…
ClassificationOrdinal ClassificationImproving the classification of extreme classes by means of loss regularisation and generalised beta distributions
An ordinal classification problem is one in which the target variable takes values on an ordinal scale. Nowadays, there are many of these problems associated with real-world tasks where it is crucial to accurately classi…
Ordinal ClassificationDIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images
In computational pathology, cancer grading has been mainly studied as a categorical classification problem, which does not utilize the ordering nature of cancer grades such as the higher the grade is, the worse the cance…
Cancer ClassificationClassificationOrdinal ClassificationOptimizing Automatic Speech Assessment: W-RankSim Regularization and Hybrid Feature Fusion Strategies
Automatic Speech Assessment (ASA) has seen notable advancements with the utilization of self-supervised features (SSL) in recent research. However, a key challenge in ASA lies in the imbalanced distribution of data, part…
Ordinal ClassificationExploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tac…
Ordinal ClassificationSentiment Analysistext-classificationText ClassificationConformal Risk Control for Ordinal Classification
As a natural extension to the standard conformal prediction method, several conformal risk control methods have been recently developed and applied to various learning problems. In this work, we seek to control the confo…
ClassificationConformal PredictionDiabetic Retinopathy DetectionOrdinal ClassificationDistribution-free Conformal Prediction for Ordinal Classification
Conformal prediction is a general distribution-free approach for constructing prediction sets combined with any machine learning algorithm that achieve valid marginal or conditional coverage in finite samples. Ordinal cl…
ClassificationConformal PredictionOrdinal ClassificationPrediction+1Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification
Emotion detection in textual data has received growing interest in recent years, as it is pivotal for developing empathetic human-computer interaction systems. This paper introduces a method for categorizing emotions fro…
ClassificationEmotion ClassificationOrdinal ClassificationDistilling Privileged Multimodal Information for Expression Recognition using Optimal Transport
Deep learning models for multimodal expression recognition have reached remarkable performance in controlled laboratory environments because of their ability to learn complementary and redundant semantic information. How…
DiversityKnowledge DistillationOrdinal ClassificationFusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Deep learning enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain. These descriptors provide fine-grained information that physici…
Ordinal Classification