Multi-Label Classification
375 papers with code • 10 benchmarks • 28 datasets
Multi-Label Classification is the supervised learning problem where an instance may be associated with multiple labels. This is an extension of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.
Benchmarks
These leaderboards are used to track progress in Multi-Label Classification
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Libraries
Use these libraries to find Multi-Label Classification models and implementationsDatasets
MS COCO
CheXpert
NUS-WIDE
ChestX-ray14
MIMIC-CXR
PASCAL VOC 2007
ECHR
Friedman1
VizWiz-Classification
Subtasks
Latest papers with no code
Tabular Learning: Encoding for Entity and Context Embeddings
Examining the effect of different encoding techniques on entity and context embeddings, the goal of this work is to challenge commonly used Ordinal encoding for tabular learning.
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
In this study, we propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersectional groups in high-dimensional chest X- ray multi-label classification.
Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
This article argues that we can identify the events more accurately by leveraging the event taxonomy.
Determined Multi-Label Learning via Similarity-Based Prompt
In this novel labeling setting, each training instance is associated with a \textit{determined label} (either "Yes" or "No"), which indicates whether the training instance contains the provided class label.
Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels
This paper focuses on open-ended video question answering, which aims to find the correct answers from a large answer set in response to a video-related question.
Neural Field Classifiers via Target Encoding and Classification Loss
We successfully propose a novel Neural Field Classifier (NFC) framework which formulates existing neural field methods as classification tasks rather than regression tasks.
Embedded Multi-label Feature Selection via Orthogonal Regression
Additionally, both global feature redundancy and global label relevancy information have been considered in the orthogonal regression model, which could contribute to the search for discriminative and non-redundant feature subsets in the multi-label data.
Automated Machine Learning for Multi-Label Classification
Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand.
Improving Neural-based Classification with Logical Background Knowledge
We develop a new multi-scale methodology to evaluate how the benefits of a neurosymbolic technique evolve with the scale of the network.
Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)
A novel imbalance-aware loss function is also proposed, improving the multi-label classification performance of the model by making it more robust to data imbalance.
