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Arxiv Doc - Capsule networks - Classification: label correlation - Frequently cited paper - GitHub project - In-context learning - k-nearest neighbors - Multi-label classification - Multi-label Text classification - NLP@Amazon - NLP@Facebook - NLP 4 Semanlink - Omar Khattab - Raphaël Sourty - Rare events - Transformers - Tweet -

Favoris ; Machine learning: techniques AND Multi-label classification

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8 Documents

[2401.12178] In-Context Learning for Extreme Multi-Label Classification

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2024-03-17 About

Lewis Tunstall sur Twitter : "The SetFit library for few-shot learning with Sentence Transformers now supports *multi-label text classification*..."

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2022-10-14 About

X-BERT: eXtreme Multi-label Text Classification using Bidirectional Encoder Representations from Transformers

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2021-01-10 About

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 About

Hierarchical Multi-label Classification of Text with Capsule Networks (2019)

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2020-08-15 About

[2003.11644] MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network

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2020-08-14 About

[Learning Note] StarSpace For Multi-label Text Classification

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2018-09-11 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About