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scikit-multilearn is a Python library for performing multi-label classification.
The WEKA data mining software: An update
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten · 1931
Earlier work this paper cites.
Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
Joseph B Kruskal · 1964
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Support vector machines
Marti A. Hearst, Susan T Dumais, Edgar Osuna, John Platt, and Bernhard Scholkopf · 1998
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Gabor Csardi and Tamas Nepusz · 2006
Earlier work this paper cites.
A guide to NumPy , volume 1
Travis E Oliphant · 2006
Earlier work this paper cites.
Python for scientific computing
Travis E Oliphant · 2007
Earlier work this paper cites.
Ml-knn: A lazy learning approach to multi-label learning
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Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
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Effective and efficient multilabel classification in domains with large number of labels
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Classifier chains for multi-label classification
Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank · 2009
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Yanmin Sun, Andrew KC Wong, and Mohamed S Kamel · 2009
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Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas · 2009
Earlier work this paper cites.
Bayes optimal multilabel classification via probabilistic classifier chains
Krzysztof Dembczynski, Weiwei Cheng, and Eyke Hüllermeier · 2010
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Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
Salvador García, Alberto Fernández, Julián Luengo, and Francisco Herrera · 2010
Earlier work this paper cites.
Multi-label classification and extracting predicted class hierarchies
Florian Brucker, Fernando Benites, and Elena Sapozhnikova · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
On the stratification of multi-label data
Konstantinos Sechidis, Grigorios Tsoumakas, and Ioannis Vlahavas · 2011
Earlier work this paper cites.
Feature-aware label space dimension reduction for multi-label classification
Yao-Nan Chen and Hsuan-Tien Lin · 2012
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An extensive experimental comparison of methods for multi-label learning
Gjorgji Madjarov, Dragi Kocev, Dejan Gjorgjevikj, and Sašo Džeroski · 2012
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Multilabel classification with principal label space transformation
Farbound Tai and Hsuan-Tien Lin · 2012
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R: A Language and Environment for Statistical Computing
R Core Team · 2013
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Efficient monte carlo optimization for multi-label classifier chains
Jesse Read, Luca Martino, and David Luengo · 2013
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How is a data-driven approach better than random choice in label space division for multi-label classification?
Piotr Szymański, Tomasz Kajdanowicz, and Kristian Kersting · 2016
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How is a data-driven approach better than random choice in label space division for multi-label classification?
Piotr Szymański, Tomasz Kajdanowicz, and Kristian Kersting · 2016
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Cnn-rnn: A unified framework for multi-label image classification
Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu · 2016
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Hcp: A flexible cnn framework for multi-label image classification
Yunchao Wei, Wei Xia, Min Lin, Junshi Huang, Bingbing Ni, Jian Dong, Yao Zhao, and Shuicheng Yan · 2016
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GNU Octave version 4.2.1 manual: a high-level interactive language for numerical computations , 2017
John W. Eaton, David Bateman, Søren Hauberg, and Rik Wehbring · 2017
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Multi-label classification via feature-aware implicit label space encoding
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Dependent binary relevance models for multi-label classification
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The graph-tool python library
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