Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Multi label classification of Artificial Intelligence related patents using Modified D2SBERT and Sentence Attention mechanism
Patent classification is an essential task in patent information management and patent knowledge mining. It is very important to classify patents related to artificial intelligence, which is the biggest topic these days.…
Deep LearningManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy
Extreme multi-label text classification utilizes the label hierarchy to partition extreme labels into multiple label groups, turning the task into simple multi-group multi-label classification tasks. Current research enc…
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals
Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their pre…
ClassificationClinical KnowledgeMedical Image AnalysisMulti-Label Classification+1Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention
Most user-related data can be represented as a sequence of events associated with a timestamp and a collection of categorical labels. For example, the purchased basket of goods and the time of buying fully characterize t…
ClassificationMulti-Label ClassificationFLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning
Federated learning aims to share private data to maximize the data utility without privacy leakage. Previous federated learning research mainly focuses on multi-class classification problems. However, multi-label classif…
ClassificationFederated LearningMulti-class ClassificationMulti-Label Classification+1Causality Compensated Attention for Contextual Biased Visual Recognition
Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that th…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image ClassificationObjectStudyFormer : Attention-Based and Dynamic Multi View Classifier for X-ray images
Chest X-ray images are commonly used in medical diagnosis, and AI models have been developed to assist with the interpretation of these images. However, many of these models rely on information from a single view of the …
Classificationimage-classificationImage ClassificationMedical Diagnosis+2Solution for the EPO CodeFest on Green Plastics: Hierarchical multi-label classification of patents relating to green plastics using deep learning
This work aims at hierarchical multi-label patents classification for patents disclosing technologies related to green plastics. This is an emerging field for which there is currently no classification scheme, and hence,…
ClassificationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONInterval Type-2 Fuzzy Neural Networks for Multi-Label Classification
Prediction of multi-dimensional labels plays an important role in machine learning problems. We found that the classical binary labels could not reflect the contents and their relationships in an instance. Hence, we prop…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVocal Bursts Type PredictionSketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Leveraging the kernel trick in both the input and output spaces, surrogate kernel methods are a flexible and theoretically grounded solution to structured output prediction. If they provide state-of-the-art performance o…
Multi-Label ClassificationStructured PredictionSimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware me…
ClassificationFairnessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCluster-Guided Label Generation in Extreme Multi-Label Classification
For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent …
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONReview of Extreme Multilabel Classification
Extreme multi-label classification or XMLC, is an active area of interest in machine learning. Compared to traditional multi-label classification, here the number of labels is extremely large, hence, the name extreme mul…
Classificationcompressed sensingExtreme Multi-Label ClassificationMulti-Label Classification+1Towards Geospatial Foundation Models via Continual Pretraining
Geospatial technologies are becoming increasingly essential in our world for a wide range of applications, including agriculture, urban planning, and disaster response. To help improve the applicability and performance o…
Change DetectionContinual PretrainingDisaster ResponseDiversity+4Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs
This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. Our working hypothesis is that distributional sparsity, as facil…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONX-ray ClassificationDeep Dependency Networks for Multi-Label Classification
We propose a simple approach which combines the strengths of probabilistic graphical models and deep learning architectures for solving the multi-label classification task, focusing specifically on image and video data. …
Action ClassificationClassificationimage-classificationImage Classification+3Behavioural Reports of Multi-Stage Malware
The extensive damage caused by malware requires anti-malware systems to be constantly improved to prevent new threats. The current trend in malware detection is to employ machine learning models to aid in the classificat…
feature selectionIntrusion DetectionMalware DetectionMulti-Label Classification+1LSTM and CNN application for core-collapse supernova search in gravitational wave real data
$Context.$ Core-collapse supernovae (CCSNe) are expected to emit gravitational wave signals that could be detected by current and future generation interferometers within the Milky Way and nearby galaxies. The stochastic…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTime Series AnalysisMulti-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains
This paper proposes an adaptive graph-based approach for multi-label image classification. Graph-based methods have been largely exploited in the field of multi-label classification, given their ability to model label co…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Chains of Autoreplicative Random Forests for missing value imputation in high-dimensional datasets
Missing values are a common problem in data science and machine learning. Removing instances with missing values can adversely affect the quality of further data analysis. This is exacerbated when there are relatively ma…
DenoisingImputationMissing ValuesMulti-Label Classification+1