Random k -Labelsets: An Ensemble Method for Multilabel Classification

By Grigorios Tsoumakas, Ioannis P. Vlahavas. 2007

1,001 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Evaluation & Benchmarks

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Citations in the corpus

Elucidating image-to-set prediction: An analysis of models, losses and datasets (2019, methodology): “In order to explicitly capture label co-occurrences, powersets [46] and methods learning the joint probability distribution of labels have been introduced in the literature.”
Learning shared subspace for multi-label dimensionality reduction via dependence maximization (2015, methodology): “Algorithms belonging to this group include RAkEL [22] and ensembles of classifier chains [23].”
Ensemble deep learning: A review (2021, methodology): “It can also be combined with ensemble learning, and a typical application is the RAndom k-labELsets (RAKEL) algorithm [189].”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “It is an approach that has been successfully used as base transformation in classification algorithms such as RAkEL [34] and HOMER [35].”
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013, background): “Almost all the other work on large category multi-label classification deals with thousands of labels [21,24,41,42] and it is not immediately clear that these techniques will scale to millions of labels and beyond.”
Lift: Multi-Label Learning with Label-Specific Features (2011, background): “Specifically, a family of q functions {f1, f2, · · · , fq} are learned by exploring the influences among a subset of functions {fk1 , fk2 , · · · , fkq′ } (q ′ ≤ q) [Tsoumakas and Vlahavas, 2007; Read et al., 2009; Zhang and Zhang, 2010] or among all the functions [Yan et al.”
A Literature Survey on Algorithms for Multi-label Learning (2010, methodology): “[54] is an ensemble method that iteratively constructs an ensemble of some (# of models desired, m) Label Powerset(LP) clasifiers.”
On label dependence and loss minimization in multi-label classification (2012, methodology): “The RAKEL algorithm (Tsoumakas and Vlahavas 2007) is an ensemble method that consists of several LP classifiers defined on randomly drawn subsets of labels.”
Learning to diagnose from scratch by exploiting dependencies among labels (2017, background): “Researchers have sought to model inter-label dependencies by making predictions over the label power set (e.g. Tsoumakas & Vlahavas (2007) and Read et al. (2008)), by training classiﬁers with loss functions that implicitly represent dependencies (e.g. Li et al. (2017)), and by using a sequence of…”
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation (2015, methodology): “There are many proposals in this field, from MLC trees like ML-TREE [20] or a multilabel kNN called ML-kNN [21] to multilabel neural networks [22,23] and SVMs [24].”
An extensive experimental comparison of methods for multi-label learning (2012, methodology): “Decision trees : Clare et al. [11] adapted the C4.5 algorithm for multi-label data (ML-C4.5) by modifying the formula for calculating entropy.”
Scalable and efficient multi-label classification for evolving data streams (2012, methodology): “On a window of N examples: Log-Loss = 1 NL N∑ i=1 L∑ j=1 min(log-loss(ŷ(i)j , y (i) j ), ln(N)) where: log-loss(ŷ, y) = −(ln(ŷ)y + ln(1 − ŷ)(1 − y)) All measures have been reviewed in Read (2010) and are commonly used in the literature (e.g. Tsoumakas and Katakis 2007; Tsoumakas and Vlahavas 200”
Multi-Label Classification of Music into Emotions (2008, methodology): “We compared the following multilabel classiﬁcation algorithms: binary relevance (BR), label powerset (LP), random k -labelsets (RAKEL) [15] and multilabel k-nearest neighbor (ML k NN) [21].”
Transductive Multilabel Learning via Label Set Propagation (2013, methodology): “Extension of other traditional learning techniques have also been studied, such as probabilistic generative models [24], [31], decision trees [8], neural networks [34], maximal margin methods [15], [20], maximum entropy methods [14], [40] and ensemble methods [12].”
Decision trees for hierarchical multi-label classification (2008, background): “A first approach to obtain an overall performance score is to construct an overall PR curve by transforming the multilabel problem into a binary problem as follows [39, 36, 7].”