Extreme Multi-Label Classification
29 papers with code • 0 benchmarks • 2 datasets
Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.
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Libraries
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EXTREME CLASSIFICATION
LSHTC
Most implemented papers
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
wushanshan/L1AE
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Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a factor of 1. 1-3x) compared to the previous state-of-the-art methods.
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
The lower the HS level, the less the categorization performance.
A no-regret generalization of hierarchical softmax to extreme multi-label classification
mwydmuch/extremeText
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NeurIPS 2018
Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework
wing-nus/jd2skills-bert-xmlc
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COLING 2020
We introduce a deep learning model to learn the set of enumerated job skills associated with a job description.
Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy
We show that our algorithm has a regret guarantee of $O(k\sqrt{(A-k+1)T \log (|\mathcal{F}|T)})$, where $A$ is the total number of arms and $\mathcal{F}$ is the class containing the regression function, while only requiring $\tilde{O}(A)$ computation per time step.
Stratified Sampling for Extreme Multi-Label Data
Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data.
Priberam at MESINESP Multi-label Classification of Medical Texts Task
Information retrieval tools are crucial in order to navigate and provide meaningful recommendations for articles and treatments.
Extreme Multi-label Learning for Semantic Matching in Product Search
amzn/pecos
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In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products.
ECLARE: Extreme Classification with Label Graph Correlations
Extreme-classification/ECLARE
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This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.
DECAF: Deep Extreme Classification with Label Features
Extreme-classification/DECAF
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This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.
