Papers Multi-class Classification
“Multi-class Classification” 태그가 달린 논문 1,009편 · 필터 해제
Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives
We introduce a dataset for classifying wellness dimensions in social media user posts, covering six key aspects: physical, emotional, social, intellectual, spiritual, and vocational. The dataset is designed to capture th…
Multi-class ClassificationYour Pretrained Model Tells the Difficulty Itself: A Self-Adaptive Curriculum Learning Paradigm for Natural Language Understanding
Curriculum learning is a widely adopted training strategy in natural language processing (NLP), where models are exposed to examples organized by increasing difficulty to enhance learning efficiency and performance. Howe…
Natural Language UnderstandingMulti-class ClassificationAutomated Multi-Class Crop Pathology Classification via Convolutional Neural Networks: A Deep Learning Approach for Real-Time Precision Agriculture
Crop diseases present a significant barrier to agricultural productivity and global food security, especially in large-scale farming where early identification is often delayed or inaccurate. This research introduces a C…
Multi-class ClassificationImage ClassificationFair Domain Generalization: An Information-Theoretic View
Domain generalization (DG) and algorithmic fairness are two critical challenges in machine learning. However, most DG methods focus only on minimizing expected risk in the unseen target domain without considering algorit…
Multi-class ClassificationDomain GeneralizationMulti-modal Representations for Fine-grained Multi-label Critical View of Safety Recognition
The Critical View of Safety (CVS) is crucial for safe laparoscopic cholecystectomy, yet assessing CVS criteria remains a complex and challenging task, even for experts. Traditional models for CVS recognition depend on vi…
Multi-class ClassificationBinary ClassificationTopological Signatures vs. Gradient Histograms: A Comparative Study for Medical Image Classification
This work presents a comparative evaluation of two fundamentally different feature extraction paradigms--Histogram of Oriented Gradients (HOG) and Topological Data Analysis (TDA)--for medical image classification, with a…
Medical Image ClassificationMulti-class ClassificationBinary ClassificationDetecting immune cells with label-free two-photon autofluorescence and deep learning
Label-free imaging has gained broad interest because of its potential to omit elaborate staining procedures which is especially relevant for in vivo use. Label-free multiphoton microscopy (MPM), for instance, exploits tw…
Binary ClassificationClassificationFADMulti-class Classification+1SHORE: A Long-term User Lifetime Value Prediction Model in Digital Games
In digital gaming, long-term user lifetime value (LTV) prediction is essential for monetization strategy, yet presents major challenges due to delayed payment behavior, sparse early user data, and the presence of high-va…
Multi-class ClassificationPredictionValue predictionFedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
With emerging application of Federated Learning (FL) in decision-making scenarios, it is imperative to regulate model fairness to prevent disparities across sensitive groups (e.g., female, male). Current research predomi…
FairnessFederated LearningMulti-class ClassificationGeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models
Classifying geospatial imagery remains a major bottleneck for applications such as disaster response and land-use monitoring-particularly in regions where annotated data is scarce or unavailable. Existing tools (e.g., RS…
ClassificationDisaster Responseimage-classificationImage Classification+6Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition
The increasing complexity of rotating machinery and the diversity of operating conditions, such as rotating speed and varying torques, have amplified the challenges in fault diagnosis in scenarios requiring domain adapta…
DiagnosticDomain AdaptationFault DiagnosisMulti-class ClassificationFinTagging: An LLM-ready Benchmark for Extracting and Structuring Financial Information
We introduce FinTagging, the first full-scope, table-aware XBRL benchmark designed to evaluate the structured information extraction and semantic alignment capabilities of large language models (LLMs) in the context of X…
Concept AlignmentMulti-class ClassificationLeveraging Cascaded Binary Classification and Multimodal Fusion for Dementia Detection through Spontaneous Speech
This paper presents our submission to the PROCESS Challenge 2025, focusing on spontaneous speech analysis for early dementia detection. For the three-class classification task (Healthy Control, Mild Cognitive Impairment,…
Binary ClassificationClassificationEnsemble LearningMulti-class Classification+1Detection of Suicidal Risk on Social Media: A Hybrid Model
Suicidal thoughts and behaviors are increasingly recognized as a critical societal concern, highlighting the urgent need for effective tools to enable early detection of suicidal risk. In this work, we develop robust mac…
Data AugmentationMulti-class ClassificationDECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer
Background: Accurate assessment of metastatic burden in axillary lymph nodes is crucial for guiding breast cancer treatment decisions, yet conventional imaging modalities struggle to differentiate metastatic burden level…
DiagnosticMulti-class ClassificationAppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios
Recent advances in LegalAI have primarily focused on individual case judgment analysis, often overlooking the critical appellate process within the judicial system. Appeals serve as a core mechanism for error correction …
Decision MakingMulti-class ClassificationText ClassificationText GenerationSelf-Classification Enhancement and Correction for Weakly Supervised Object Detection
In recent years, weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost. The success of recent WSOD models is often ascribed to the two-stage multi-class classification (MCC) …
Binary ClassificationClassificationMulti-class ClassificationMultiple Instance Learning+3Neuro-Argumentative Learning with Case-Based Reasoning
We introduce Gradual Abstract Argumentation for Case-Based Reasoning (Gradual AA-CBR), a data-driven, neurosymbolic classification model in which the outcome is determined by an argumentation debate structure that is lea…
Abstract ArgumentationMulti-class ClassificationVenusX: Unlocking Fine-Grained Functional Understanding of Proteins
Deep learning models have driven significant progress in predicting protein function and interactions at the protein level. While these advancements have been invaluable for many biological applications such as enzyme en…
Binary ClassificationMulti-class ClassificationEfficient Malicious UAV Detection Using Autoencoder-TSMamba Integration
Malicious Unmanned Aerial Vehicles (UAVs) present a significant threat to next-generation networks (NGNs), posing risks such as unauthorized surveillance, data theft, and the delivery of hazardous materials. This paper p…
MambaMulti-class Classification