Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
FAIRification of MLC data
The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and methods that appear in the literature. Hence, …
BenchmarkingManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONExplainable Model-specific Algorithm Selection for Multi-Label Classification
Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest in different application domains such as …
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEnhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning
Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification models suffer from event-related biases an…
Hierarchical Multi-label ClassificationLanguage ModelingLanguage ModellingManagement+4PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection
Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion. Most previous methods r…
Binary ClassificationInstance SegmentationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions from seen states and objects. The disparity between the manually labeled semantic information and its actual visual features causes a signific…
Compositional Zero-Shot LearningInductive BiasMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Deep Smart Contract Intent Detection
In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of crypto assets on blockchains. Despite early successes in this area, detec…
Intent DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentence+1Scaling Native Language Identification with Transformer Adapters
Native language identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is useful for a variety of purposes inc…
Language IdentificationMarketingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification
By distributing the training process, local approximation reduces the cost of the standard Gaussian Process. An ensemble technique combines local predictions from Gaussian experts trained on different partitions of the d…
Gaussian ProcessesMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVector-Valued Least-Squares Regression under Output Regularity Assumptions
We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output. We derive learning bounds for our method, and study under which setting statistical performance…
Image ReconstructionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONregression+1An Automatic ICD Coding Network Using Partition-Based Label Attention
International Classification of Diseases (ICD) is a global medical classification system which provides unique codes for diagnoses and procedures appropriate to a patient's clinical record. However, manual coding by huma…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONExpressing linear equality constraints in feedforward neural networks
We seek to impose linear, equality constraints in feedforward neural networks. As top layer predictors are usually nonlinear, this is a difficult task if we seek to deploy standard convex optimization methods and strong …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONHyperparameter optimization in deep multi-target prediction
As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations l…
AutoMLBenchmarkingHyperparameter OptimizationMatrix Completion+6Predicting User-specific Future Activities using LSTM-based Multi-label Classification
User-specific future activity prediction in the healthcare domain based on previous activities can drastically improve the services provided by the nurses. It is challenging because, unlike other domains, activities in h…
Activity PredictionActivity RecognitionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONHierarchical Multi-Label Classification of Scientific Documents
Automatic topic classification has been studied extensively to assist managing and indexing scientific documents in a digital collection. With the large number of topics being available in recent years, it has become nec…
ClassificationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid
Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For in…
Classificationimage-classificationImage ClassificationMulti-Label Classification+4IITD at the WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection
We present our system for the two subtasks of the shared task on propaganda detection in Arabic, part of WANLP'2022. Subtask 1 is a multi-label classification problem to find the propaganda techniques used in a given twe…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPropaganda detectionXLM-RCascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent approaches, such as XR-Transformer and Li…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product
In the remote sensing community, Land Use Land Cover (LULC) classification with satellite imagery is a main focus of current research activities. Accurate and appropriate LULC classification, however, continues to be a c…
Change DetectionClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity
Decoding emotional states from human brain activity plays an important role in brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category …
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAn Effective Approach for Multi-label Classification with Missing Labels
Compared with multi-class classification, multi-label classification that contains more than one class is more suitable in real life scenarios. Obtaining fully labeled high-quality datasets for multi-label classification…
ClassificationMissing LabelsMulti-class ClassificationMulti-Label Classification+2