Semanlink - Multi-label classification

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Classification

Wikipedia Multi-label classification

In multi-label classification, each sample can be associated with a set of class labels. It is distinct from multi-class classification which aims to predict a single mutually exclusive label.

Performance evaluation of multi-label learning system: [see here](doc:?uri=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fsummary%3Fdoi%3D10.1.1.89.7148)

Methods (non-exhaustive list):

- Dividing the original multi-label classification problem into multiple independent binary classification tasks
    - computationally expensive
    - cannot identify the correlation between label information
- KNN based. 
- Label embedding based approaches (deriving a latent label space with reduced dimensionality). Ex : [SLEEC](doc:2020/08/sparse_local_embeddings_for_ext)
- Tree-based methods: a tree structure to divide the document set recursively, so that documents in each leaf node share similar label distribution. Ex: FastXML
   - correlation between the labels can be implicitly exploited
- Deep Learning based methods: cf. successes of deep learning for text representation
- Combining label embeddings and deep learning on text to construct label-aware representation of documents. Ex [LAHA](doc:2019/06/_1905_10070_label_aware_docume)

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Απόγονοι

15 Έγγραφα (Όλα τα έγγραφα)

scikit-multilearn: Multi-Label Classification in Python

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2020-09-16 About

Mulan: A Java library for multi-label learning

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2020-08-30 About

[1812.02956] LNEMLC: Label Network Embeddings for Multi-Label Classification

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2020-08-12 About

scikit-multilearn/scikit-multilearn: A scikit-learn based module for multi-label et. al. classification

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2020-08-12 About

A novel multi-label classification algorithm based on K-nearest neighbor and random walk - Zhen-Wu Wang, Si-Kai Wang, Ben-Ting Wan, William Wei Song, 2020

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2020-08-11 About

Supplementary : Extreme Multi-label Learning with Label Features for Warm-start Tagging, Ranking & Recommendation

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2018-08-07 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

Multiclass and multilabel algorithms — scikit-learn documentation

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2018-03-17 About

Label Embedding Trees for Large Multi-Class Tasks (2010)

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2018-03-16 About

[1707.00418] Learning Deep Latent Spaces for Multi-Label Classification

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2018-03-16 About

Examination of machine learning methods for multi-label classification of intellectual property documents (2017)

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