Multi-Label Classification
369 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.
Libraries
Use these libraries to find Multi-Label Classification models and implementationsDatasets
Subtasks
Latest papers
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Multi-label classification poses challenges due to imbalanced and noisy labels in training data.
Language-Guided Transformer for Federated Multi-Label Classification
Nevertheless, it is still challenging for FL to deal with user heterogeneity in their local data distribution in the real-world FL scenario, and this issue becomes even more severe in multi-label image classification.
Adaptive Hinge Balance Loss for Document-Level Relation Extraction
In this paper, we propose to downweight the easy negatives by utilizing a distance between the classification threshold and the predicted score of each relation.
Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs.
Scalable Label Distribution Learning for Multi-Label Classification
Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios.
Category-Wise Fine-Tuning for Image Multi-label Classification with Partial Labels
A single model submitted to the competition server for the official evaluation achieves mAUC 91. 82% on the test set, which is the highest single model score in the leaderboard and literature.
VALUED -- Vision and Logical Understanding Evaluation Dataset
In order to address this, we present the VALUE (Vision And Logical Understanding Evaluation) Dataset, consisting of 200, 000$+$ annotated images and an associated rule set, based on the popular board game - chess.
ICXML: An In-Context Learning Framework for Zero-Shot Extreme Multi-Label Classification
This paper focuses on the task of Extreme Multi-Label Classification (XMC) whose goal is to predict multiple labels for each instance from an extremely large label space.
Generalized test utilities for long-tail performance in extreme multi-label classification
As such, it is characterized by long-tail labels, i. e., most labels have very few positive instances.
Exploring Best Practices for ECG Signal Processing in Machine Learning
In this work we apply down-sampling, normalization, and filtering functions to 3 different multi-label ECG datasets and measure their effects on 3 different high-performing time-series classifiers.