Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics
Scientific multi-label text classification suffers from extreme class imbalance, where specialized terminology exhibits severe power-law distributions that challenge standard classification approaches. Existing scientifi…
Multi-Label Text ClassificationMulti-Label ClassificationDomain AdaptationCXR-LT 2026 Challenge: Projection-Aware Multi-Label and Zero-Shot Chest X-Ray Classification
This challenge tackles multi-label classification for known chest X-ray (CXR) lesions and zero-shot classification for unseen ones. To handle diverse CXR projections, we integrate projection-specific models via a classif…
Multi-Label ClassificationZero-shot GeneralizationContrastive LearningStructural Stress and Learned Helplessness in Afghanistan: A Multi-Layer Analysis of the AFSTRESS Dari Corpus
We introduce AFSTRESS, the first multi-label corpus of self-reported stress narratives in Dari (Eastern Persian), comprising 737 responses collected from Afghan individuals during an ongoing humanitarian crisis. Particip…
Multi-Label ClassificationUncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models
While Vision-Language Models (VLMs) have achieved remarkable performance, their Euclidean embeddings remain limited in capturing hierarchical relationships such as part-to-whole or parent-child structures, and often face…
Multi-Label ClassificationSemi-Supervised Learning with Balanced Deep Representation Distributions
Semi-Supervised Text Classification (SSTC) mainly works under the spirit of self-training. They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseud…
Semi-Supervised Text ClassificationMulti-Label ClassificationMulti-class ClassificationDetecting Basic Values in A Noisy Russian Social Media Text Data: A Multi-Stage Classification Framework
This study presents a multi-stage classification framework for detecting human values in noisy Russian language social media, validated on a random sample of 7.5 million public text posts. Drawing on Schwartz's theory of…
Multi-Label ClassificationRARE disease detection from Capsule Endoscopic Videos based on Vision Transformers
This work is corresponding to the Gastro Competition for multi-label classification from capsule endoscopic videos (CEV). Deep learning network based on Transformers are fined-tune for this task. The based online mode is…
Multi-Label ClassificationExploring label correlations using decision templates for ensemble of classifier chains
The use of ensemble-based multi-label methods has been shown to be effective in improving multi-label classification results. One of the most widely used ensemble-based multi-label classifiers is Ensemble of Classifier C…
Multi-Label ClassificationA Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
Transcription factors (TFs) regulate gene expression through complex and co-operative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactions is not fully understood yet. Most cur…
Multi-Label ClassificationBinary ClassificationMulti-Label LearningAn Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously?
Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus …
Multi-Label Text ClassificationMulti-Label ClassificationLoRA-MME: Multi-Model Ensemble of LoRA-Tuned Encoders for Code Comment Classification
Code comment classification is a critical task for automated software documentation and analysis. In the context of the NLBSE'26 Tool Competition, we present LoRA-MME, a Multi-Model Ensemble architecture utilizing Parame…
parameter-efficient fine-tuningMulti-Label ClassificationFEAST: Retrieval-Augmented Multi-Hierarchical Food Classification for the FoodEx2 System
Hierarchical text classification (HTC) and extreme multi-label classification (XML) tasks face compounded challenges from complex label interdependencies, data sparsity, and extreme output dimensions. These challenges ar…
Multi-Label ClassificationText ClassificationMetric LearningOverview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on closed-set classes from single institution…
Multi-Label ClassificationA Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining
The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale Multi-Label Classification (MLC) in real-world data mining applications. Existing r…
Multi-Label ClassificationMulti-Label LearningHandling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning
Chest X-Ray (CXR) classification in clinical practice is often limited by imperfect supervision, arising from (i) extreme long-tailed multi-label disease distributions and (ii) missing annotations for rare or previously …
Multi-Label ClassificationMulti-Label LearningZero-Shot LearningCurriculum Learning and Pseudo-Labeling Improve the Generalization of Multi-Label Arabic Dialect Identification Models
Being modeled as a single-label classification task for a long time, recent work has argued that Arabic Dialect Identification (ADI) should be framed as a multi-label classification task. However, ADI remains constrained…
Multi-Label ClassificationQuasi-multimodal-based pathophysiological feature learning for retinal disease diagnosis
Retinal diseases spanning a broad spectrum can be effectively identified and diagnosed using complementary signals from multimodal data. However, multimodal diagnosis in ophthalmic practice is typically challenged in ter…
Diabetic Retinopathy GradingMulti-Label ClassificationData AugmentationIntegrating Multi-Label Classification and Generative AI for Scalable Analysis of User Feedback
In highly competitive software markets, user experience (UX) evaluation is crucial for ensuring software quality and fostering long-term product success. Such UX evaluations typically combine quantitative metrics from st…
Multi-Label ClassificationSentiment AnalysisFrom Generative Modeling to Clinical Classification: A GPT-Based Architecture for EHR Notes
The increasing availability of unstructured clinical narratives in electronic health records (EHRs) has created new opportunities for automated disease characterization, cohort identification, and clinical decision suppo…
Multi-Label ClassificationText ClassificationTransparency-First Medical Language Models: Datasheets, Model Cards, and End-to-End Data Provenance for Clinical NLP
We introduce TeMLM, a set of transparency-first release artifacts for clinical language models. TeMLM unifies provenance, data transparency, modeling transparency, and governance into a single, machine-checkable release …
Multi-Label Classification