Multi-Label Learning
81 papers with code • 1 benchmarks • 8 datasets
Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several possible class labels simultaneously [1]. Each of the assigned labels conveys a specific semantic relationship with the multi-label data instance [2, 3]. Multi-label learning has continued to receive a lot of research interest due to its practical application in many real-world problems such as recommender systems [4], image annotation [5], and text classification [6].
References:
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Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)
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Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
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Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38
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Bogaert M, Lootens J, Van den Poel D, Ballings M (2019) Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 279(2):620– 634
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Jing L, Shen C, Yang L, Yu J, Ng MK (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 26(10):4612–4625
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Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979
Datasets
MS COCO
ExpW
BirdSong
Animal Kingdom
MIMIC Meme Dataset
Corn Seeds Dataset
Latest papers with no code
Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection
To mitigate memory consumption, we convert the order information prediction task into a multi-label learning problem, and the inter-patch similarity prediction task into a distance matrix regression problem.
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.
Learnability Gaps of Strategic Classification
We essentially show that any learnable class is also strategically learnable: we first consider a fully informative setting, where the manipulation structure (which is modeled by a manipulation graph $G^\star$) is known and during training time the learner has access to both the pre-manipulation data and post-manipulation data.
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.
A Consistent Lebesgue Measure for Multi-label Learning
The consistency of surrogate loss functions is not proven and is exacerbated by the conflicting nature of multi-label loss functions.
Deep Learning for Multi-Label Learning: A Comprehensive Survey
Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point.
Multi-label Learning from Privacy-Label
Multi-abel Learning (MLL) often involves the assignment of multiple relevant labels to each instance, which can lead to the leakage of sensitive information (such as smoking, diseases, etc.)
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
Further, we propose a novel proximity-aware evaluation metric that enables evaluating species distribution models using any pixel-level representation of ground-truth species range map.
Can Class-Priors Help Single-Positive Multi-Label Learning?
Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label.
Exploiting Multi-Label Correlation in Label Distribution Learning
Label Distribution Learning (LDL) is a novel machine learning paradigm that assigns label distribution to each instance.
