Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media
On social media, users often express their personal feelings, which may exhibit cognitive distortions or even suicidal tendencies on certain specific topics. Early recognition of these signs is critical for effective psy…
ClassificationLanguage ModelingLanguage ModellingLarge Language Model+3Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation
The objective of topic inference in research proposals aims to obtain the most suitable disciplinary division from the discipline system defined by a funding agency. The agency will subsequently find appropriate peer rev…
FairnessHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAn Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
The pandemic resulted in vast repositories of unstructured data, including radiology reports, due to increased medical examinations. Previous research on automated diagnosis of COVID-19 primarily focuses on X-ray images,…
Computed Tomography (CT)Medical Image AnalysisMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONGKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
Multi-Label Image Recognition (MLIR) is a challenging task that aims to predict multiple object labels in a single image while modeling the complex relationships between labels and image regions. Although convolutional n…
graph constructionMulti-Label ClassificationMulti-Label Image RecognitionNetwork Embedding Using Sparse Approximations of Random Walks
In this paper, we propose an efficient numerical implementation of Network Embedding based on commute times, using sparse approximation of a diffusion process on the network obtained by a modified version of the diffusio…
ClusteringMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONNetwork EmbeddingBag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays
Clinical classification of chest radiography is particularly challenging for standard machine learning algorithms due to its inherent long-tailed and multi-label nature. However, few attempts take into account the couple…
Data AugmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRobust Asymmetric Loss for Multi-Label Long-Tailed Learning
In real medical data, training samples typically show long-tailed distributions with multiple labels. Class distribution of the medical data has a long-tailed shape, in which the incidence of different diseases is quite …
image-classificationImage ClassificationMedical Image ClassificationMulti-Label Classification+1DOST -- Domain Obedient Self-supervised Training for Multi Label Classification with Noisy Labels
The enormous demand for annotated data brought forth by deep learning techniques has been accompanied by the problem of annotation noise. Although this issue has been widely discussed in machine learning literature, it h…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Self-Supervised Learning with Scene Images
Self-supervised learning (SSL) methods targeting scene images have seen a rapid growth recently, and they mostly rely on either a dedicated dense matching mechanism or a costly unsupervised object discovery module. This …
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONObject Discovery+1A Symbolic Character-Aware Model for Solving Geometry Problems
AI has made significant progress in solving math problems, but geometry problems remain challenging due to their reliance on both text and diagrams. In the text description, symbolic characters such as "$\triangle$ABC" o…
MathMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSelf-Supervised LearningTowards Imbalanced Large Scale Multi-label Classification with Partially Annotated Labels
Multi-label classification is a widely encountered problem in daily life, where an instance can be associated with multiple classes. In theory, this is a supervised learning method that requires a large amount of labelin…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
Transfomer-based models have significantly advanced natural language processing, in particular the performance in text classification tasks. Nevertheless, these models face challenges in processing large files, primarily…
Android Malware DetectionClassificationDefect DetectionDocument Classification+7DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction
Automatic protein function prediction (AFP) is classified as a large-scale multi-label classification problem aimed at automating protein enrichment analysis to eliminate the current reliance on labor-intensive wet-lab m…
Contrastive LearningGraph AttentionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Extreme Multi-Label Skill Extraction Training using Large Language Models
Online job ads serve as a valuable source of information for skill requirements, playing a crucial role in labor market analysis and e-recruitment processes. Since such ads are typically formatted in free text, natural l…
Contrastive LearningExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEnergy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances
Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be direct…
energy managementManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguisti…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition
Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets. Existing litera…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image RecognitionYOLIC: An Efficient Method for Object Localization and Classification on Edge Devices
In the realm of Tiny AI, we introduce ``You Only Look at Interested Cells" (YOLIC), an efficient method for object localization and classification on edge devices. Through seamlessly blending the strengths of semantic se…
ClassificationComputational EfficiencyCPUMulti-Label Classification+6Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification
Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electric…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRhythmMDACE: MIMIC Documents Annotated with Code Evidence
We introduce a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. One such task is Computer-Assisted Coding (CAC) which has improved significantly in rece…
Document ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION