Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Retrieval-Augmented Classification of Environmental Mitigations in Hydropower Licensing Documents
Identifying and classifying environmental mitigation obligations in Federal Energy Regulatory Commission hydropower licensing documents is a labor-intensive task requiring deep domain expertise. We formulate this as a mu…
Multi-Label ClassificationZero-shot GeneralizationKREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for medical reimbursement, quality reporting, and clinical research. Existin…
Multi-Label ClassificationLightTeaNet: A Weakly Supervised Lightweight CNN for Multi-Label Tea Leaf Disease Detection and Localization
Tea is known as an important crop in many parts of South and Southeast Asia, yet the production of tea is still hampered by the multiple diseases that decrease the quantity and quality. Traditional methods of inspection,…
Multi-Label ClassificationObject DetectionExponential Convex Calibration Dimension for the Multi-Label Jaccard Measure
The per-instance Jaccard score, or intersection over union (IoU), is standard in multi-label classification and binary segmentation. With $s$ labels, its loss matrix has $2^s$ outcomes and reports. Under the convention $…
Multi-Label ClassificationDoes generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature
With a large controlled vocabulary as the label set, the task of automated subject indexing in a library can be understood as a multi-label classification task. If the set of subject terms is large, the problem fits the …
Multi-Label ClassificationLabel-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification
Multi-label classification assigns several co-occurring labels to each aerial scene, yet deployed models often encounter data distributions different from their training. Feature-statistics augmentation such as MixStyle,…
Multi-Label ClassificationDomain GeneralizationScene ClassificationAn Empirical Analysis of Continual Learning for Heterogeneous Medical Visual Question Answering
Deploying medical visual question answering (MedVQA) systems in real-world clinical settings requires models that adapt to new clinical tasks without forgetting previously acquired knowledge. Continual learning (CL) prov…
Multi-Label ClassificationVisual Question AnsweringContinual LearningAutomatic Thematic Indexing of Large Literary Corpora: A Machine Learning Approach to Voltaire's Complete Works
Thematic indexing -- the practice of assigning structured conceptual labels to sections of text -- is essential to scholarly access in large-scale literary and historical editions, yet it remains a largely manual, labour…
Multi-Label ClassificationMLPTR-CC: Multi-label Pathology Test Recommendation using Classifier Chains and SHAP
Diagnostic decision making often relies on a sequence of pathology tests that bridge patient symptoms and final disease diagnosis. Existing clinical decision-support systems typically focus on predicting single diseases …
Multi-Label ClassificationBeyond Independent Labels: Schwartz-Geometry Decoding for Human Value Detection
Human value detection is commonly formulated as sentence-level multi-label classification over the 19 refined Schwartz values, typically predicted as independent labels. Schwartz theory, however, describes them as a circ…
Multi-Label ClassificationStructured PredictionImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment
Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG images, remain incomplete because of short d…
Multi-Label ClassificationECG DigitizationCaresAI at SMM4H-HeaRD 2026: Predicting TNM Staging
This study aims to predict Tumor, Node, and Metastasis (TNM) stage labels independently, with the Cancer Genome Atlas (TCGA) pathology report as the sixth shared task of SMM4H-HeaRD 2026. The problem is framed as three m…
Multi-Label ClassificationTRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling
Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis. However, real clinical data often exhibit extreme long-tailed distributions, leading to degraded performance on rare disease…
Multi-Label ClassificationData AugmentationBenchmarking Large Language Models on Floating-Point Error Classification
This paper investigates the capability of Large Language Models (LLMs) to detect and classify floating-point errors statically in software code. We introduce InterFLOPBench, a benchmark of 90 C kernels with 1 130 test sa…
Multi-Label ClassificationMultilingual Polarization Detection Using Transformer-Based Models with Class Weighting and Threshold Tuning
This paper describes our submission to SemEval-2026 Task 9 on detecting multilingual, multicultural, and multievent online polarization. We address all three subtasks: binary polarization detection, polarization type cla…
Multi-Label ClassificationIntracranial Aneurysm Classification and Segmentation via Tri-Axial ROI and Multi-Task Learning
Intracranial aneurysms are often asymptomatic until rupture, which carries high mortality. Rupture risk assessment and treatment planning depend on both aneurysm morphology and anatomical location, yet existing automated…
Multi-Label ClassificationMulti-Task LearningBrain-Adapter: A Dual-Stream Vision-Language MIL Framework for Comprehensive 3D CT Diagnosis of Acute Intracranial Pathologies
Automated diagnosis of 3D brain CT scans is essential for critical care, yet it remains challenging due to the heavy reliance on manual annotations and the limited semantic understanding of conventional models. While 2D …
Multi-Label ClassificationMultiple Instance LearningSAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis
Gastrointestinal cancers represent a growing health burden in the South Asian region, driven largely by rapid changes in socio-economic conditions and lifestyle habits. However, early diagnosis remains limited by inadequ…
Multi-Label ClassificationMulti-class ClassificationVisual Question AnsweringImage CaptioningWhich Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science
Research methods are essential carriers of knowledge contribution in academic papers. Automatic multi-label classification of research methods can support knowledge services such as method retrieval, review generation, a…
Multi-Label ClassificationGDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks
Electric vehicle charging stations (EVCSs) can expose distribution feeders to cyberattacks. While machine learning methods, including graph neural networks, can localize which bus is compromised, significant challenges r…
Multi-Label ClassificationGraph Neural Network