Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Predicting Job Titles from Job Descriptions with Multi-label Text Classification
Finding a suitable job and hunting for eligible candidates are important to job seeking and human resource agencies. With the vast information about job descriptions, employees and employers need assistance to automatica…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text ClassificationMulti-Label Text Classification+2Tree-Based Dynamic Classifier Chains
Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in p…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVisual Transformers with Primal Object Queries for Multi-Label Image Classification
Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at s…
Decoderimage-classificationImage ClassificationMulti-Label Classification+5"What can I cook with these ingredients?" -- Understanding cooking-related information needs in conversational search
As conversational search becomes more pervasive, it becomes increasingly important to understand the user's underlying information needs when they converse with such systems in diverse domains. We conduct an in-situ stud…
ClassificationConversational SearchMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOn the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations
Prediction using the ground truth sounds like an oxymoron in machine learning. However, such an unrealistic setting was used in hundreds, if not thousands of papers in the area of finding graph representations. To evalua…
Graph Representation LearningMulti-Label ClassificationNode ClassificationRepresentation LearningAn Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction
We propose an effective method to improve Protein Function Prediction (PFP) utilizing hierarchical features of Gene Ontology (GO) terms. Our method consists of a language model for encoding the protein sequence and a Gra…
Hierarchical Multi-label ClassificationLanguage ModelingLanguage ModellingMulti-Label Classification+2Adaptive label thresholding methods for online multi-label classification
Existing online multi-label classification works cannot well handle the online label thresholding problem and lack the regret analysis for their online algorithms. This paper proposes a novel framework of adaptive label …
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONImproving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents
Multi-label learning predicts a subset of labels from a given label set for an unseen instance while considering label correlations. A known challenge with multi-label classification is the long-tailed distribution of la…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningGaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Multi-label classification (MLC) is a prediction task where each sample can have more than one label. We propose a novel contrastive learning boosted multi-label prediction model based on a Gaussian mixture variational a…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPredictionRecurrent Bayesian Classifier Chains for Exact Multi-Label Classification
Exact multi-label classification is the task of assigning each datapoint a set of class labels such that the assigned set exactly matches the ground truth. Optimizing for exact multi-label classification is important in …
Autonomous VehiclesClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
A wide variety of machine learning tasks such as knowledge base completion, ontology alignment, and multi-label classification can benefit from incorporating into learning differentiable representations of graphs or taxo…
Knowledge Base CompletionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning with Pairwise Relevance Ordering
Precisely annotating objects with multiple labels is costly and has become a critical bottleneck in real-world multi-label classification tasks. Instead, deciding the relative order of label pairs is obviously less labor…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningA Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning
Multi-label classification (MLC) allows complex dependencies among labels, making it more suitable to model many real-world problems. However, data annotation for training MLC models becomes much more labor-intensive due…
Active LearningInductive BiasMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis
This work presents a detailed comparison of the performance of deep learning models such as convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent units (GRU), their hybrids, and a selection …
Arabic Sentiment AnalysisDeep LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Task at ICON-2021
This paper presents the findings of the ICON-2021 shared task on Multilingual Gender Biased and Communal Language Identification, which aims to identify aggression, gender bias, and communal bias in data presented in fou…
Aggression IdentificationClassificationLanguage IdentificationMulti-Label Classification+1Speaker Embedding-aware Neural Diarization for Flexible Number of Speakers with Textual Information
Overlapping speech diarization is always treated as a multi-label classification problem. In this paper, we reformulate this task as a single-label prediction problem by encoding the multi-speaker labels with power set. …
Action DetectionActivity DetectionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs
Attention modules for Convolutional Neural Networks (CNNs) are an effective method to enhance performance on multiple computer-vision tasks. While existing methods appropriately model channel-, spatial- and self-attentio…
feature selectionImage ClassificationMulti-Label ClassificationObject Localization+2ML-Decoder: Scalable and Versatile Classification Head
In this paper, we introduce ML-Decoder, a new attention-based classification head. ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average …
ClassificationDecoderFine-Grained Image ClassificationImage Classification+3ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosi…
image-classificationImage ClassificationMedical Image AnalysisMedical Image Classification+3Hierarchical Text Classification As Sub-Hierarchy Sequence Generation
Hierarchical text classification (HTC) is essential for various real applications. However, HTC models are challenging to develop because they often require processing a large volume of documents and labels with hierarch…
ClassificationDecoderMulti-Label Classificationtext-classification+1