Mining Multi-label Data

By Grigorios Tsoumakas, Ioannis Manousos Katakis, Ioannis P. Vlahavas. 2010

1,592 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Automated Machine Learning: State-of-The-Art and Open Challenges (2019, background): “Auto − MEKAGGP [26] focuses on the AutoML task for multi-label classification problem [121] that aims to learn models from data capable of representing the relationships between input attributes and a set of class labels, where each instance may belong to more than one class.”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “There are two main approaches [1] to accomplish an MLC task: data transformation and algorithm adaptation.”
Learning More Universal Representations for Transfer-Learning (2017, background): “As detailed in [47], the evaluation of methods that learn from multi-”
Learning from Partial Labels (2011, background): “• In multi-label learning (Boutell et al., 2004; Tsoumakas et al., 2010), each example is assigned multiple labels, all of which can be true.”
Kernel Methods and Machine Learning (2014, methodology): “Typical methods include label powerset (LP) [276], binary relevance (BR) [275], ensembles of classifier chains (ECC) [216], and compressive sensing [102].”
Multi-Label Learning with Global and Local Label Correlation (2017, methodology): “In the experiments, we use the implementation in the Mulan package(7) [45].”
QUINTA: A question tagging assistant to improve the answering ratio in electronic forums (2015, methodology): “In an IR system the vector for each document is compared with the one generated by a query, usually through a common technique known as TF/IDF (Term Frequency/Inverse Document Frequency, [8]) and a similarity metric such as the cosine distance [9].”
On label dependence and loss minimization in multi-label classification (2012, methodology): “We used an implementation of BR and LP from the MULAN package (Tsoumakas et al. 2010),5 and the original implementation of CC (Read et al.”
Statistical topic models for multi-label document classification (2011, methodology): “In terms of discriminative approaches to multi-label classification, there is a large body of prior work, which has been well-summarized elsewhere in the literature (e.g., see Tsoumakas and Katakis, 2007; Tsoumakas et al., 2009).”
The Emerging Trends of Multi-Label Learning (2020, background): “MULTI-LABEL classification (MLC) [1], [2], which assigns multiple labels for each instance simultaneously, is of paramount importance in a variety of fields ranging from protein function classification and document classification, to automatic image categorization.”
SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge (2013, methodology): “However, microaverage is used to define a different metric in tasks where more than one label can be associated with each data item (Tsoumakas et al., 2010).”
Multi-label classification with a reject option (2013, methodology): “The above measures can also be defined sample-wise, by viewing a class as the set of queries that are relevant to a given sample [5].”
A Review on Multi-Label Learning Algorithms (2014, background): “Note that there have been some nice reviews on multi-label learning techniques [17], [89], [91].”