Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing
Recently deep learning has been used in Medical subject headings (MeSH) indexing to reduce the time and monetary cost by manual annotation, including DeepMeSH, TextCNN, etc. However, these models still suffer from failin…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRepresentation LearningCross-Lingual Disaster-related Multi-label Tweet Classification with Manifold Mixup
Distinguishing informative and actionable messages from a social media platform like Twitter is critical for facilitating disaster management. For this purpose, we compile a multilingual dataset of over 130K samples for …
General ClassificationManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Unbiased Loss Functions for Extreme Classification With Missing Labels
The goal in extreme multi-label classification (XMC) is to tag an instance with a small subset of relevant labels from an extremely large set of possible labels. In addition to the computational burden arising from large…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMissing Labels+3BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer
The objective of an expert recommendation system is to trace a set of candidates' expertise and preferences, recognize their expertise patterns, and identify experts. In this paper, we introduce a multimodal classificati…
ArticlesCommunity Question AnsweringGeneral ClassificationMulti-Label Classification+3Learning Gradient Boosted Multi-label Classification Rules
In multi-label classification, where the evaluation of predictions is less straightforward than in single-label classification, various meaningful, though different, loss functions have been proposed. Ideally, the learni…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONApproaches For Multi-View Redescription Mining
The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This intere…
AttributeBIG-bench Machine LearningClusteringMulti-Label Classification+2An Integer Linear Programming Framework for Mining Constraints from Data
Structured output prediction problems (e.g., sequential tagging, hierarchical multi-class classification) often involve constraints over the output label space. These constraints interact with the learned models to filte…
Multi-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONExtreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains
Classifier chains is a key technique in multi-label classification, since it allows to consider label dependencies effectively. However, the classifiers are aligned according to a static order of the labels. In the conce…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOnline Metric Learning for Multi-Label Classification
Existing research into online multi-label classification, such as online sequential multi-label extreme learning machine (OSML-ELM) and stochastic gradient descent (SGD), has achieved promising performance. However, thes…
ClassificationGeneral ClassificationMetric LearningMulti-Label Classification+1Modeling Label Semantics for Predicting Emotional Reactions
Predicting how events induce emotions in the characters of a story is typically seen as a standard multi-label classification task, which usually treats labels as anonymous classes to predict. They ignore information tha…
Emotion ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONLearning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within…
Extreme Multi-Label ClassificationLearning-To-RankMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this paper, we shift the attention to multi-la…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONExploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected pa…
COVID-19 DiagnosisGeneral ClassificationInterpretability Techniques for Deep LearningInterpretable Machine Learning+2A multimodal approach for multi-label movie genre classification
Movie genre classification is a challenging task that has increasingly attracted the attention of researchers. In this paper, we addressed the multi-label classification of the movie genres in a multimodal way. For this …
ClassificationGeneral ClassificationGenre classificationMulti-Label Classification+1InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
The skip-gram model for learning word embeddings (Mikolov et al. 2013) has been widely popular, and DeepWalk (Perozzi et al. 2014), among other methods, has extended the model to learning node representations from networ…
Learning Word EmbeddingsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONWord EmbeddingsMinimizing Supervision in Multi-label Categorization
Multiple categories of objects are present in most images. Treating this as a multi-class classification is not justified. We treat this as a multi-label classification problem. In this paper, we further aim to minimize …
Active LearningGeneral ClassificationMulti-class ClassificationMulti-Label Classification+1Classification and Clustering of arXiv Documents, Sections, and Abstracts, Comparing Encodings of Natural and Mathematical Language
In this paper, we show how selecting and combining encodings of natural and mathematical language affect classification and clustering of documents with mathematical content. We demonstrate this by using sets of document…
ClassificationClusteringGeneral ClassificationMath+2Interaction Matching for Long-Tail Multi-Label Classification
We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be useful for ad-hoc search result ranking. …
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA Robust Experimental Evaluation of Automated Multi-Label Classification Methods
Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially …
AutoMLBayesian OptimizationClassificationGeneral Classification+2Fast Network Embedding Enhancement via High Order Proximity Approximation
Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently. In this paper, we summarize most existing NRL methods into a unified two-step fram…
Dimensionality ReductionLink PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3