MULAN: a Java library for multi-label learning. MULAN is a Java library for learning from multi-label data. It offers a variety of classification, ranking, thresholding and dimensionality reduction algorithms, as well as algorithms for learning from hierarchically structured labels. In addition, it contains an evaluation framework that calculates a rich variety of performance measures.

References in zbMATH (referenced in 39 articles )

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  1. Francisco Charte, Antonio J. Rivera, David Charte, María J. del Jesus, Francisco Herrera: Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository (2018) arXiv
  2. Wei, Tong; Guo, Lan-Zhe; Li, Yu-Feng; Gao, Wei: Learning safe multi-label prediction for weakly labeled data (2018)
  3. Huang, Kuan-Hao; Lin, Hsuan-Tien: Cost-sensitive label embedding for multi-label classification (2017)
  4. Liu, Weiwei; Tsang, Ivor W.: Making decision trees feasible in ultrahigh feature and label dimensions (2017)
  5. Liu, Weiwei; Tsang, Ivor W.; Müller, Klaus-Robert: An easy-to-hard learning paradigm for multiple classes and multiple labels (2017)
  6. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  7. Wu, Yu-Ping; Lin, Hsuan-Tien: Progressive random $k$-labelsets for cost-sensitive multi-label classification (2017)
  8. Arias, Jacinto; Gamez, Jose A.; Nielsen, Thomas D.; Puerta, Jose M.: A scalable pairwise class interaction framework for multidimensional classification (2016)
  9. Ghouti, Lahouari: A new kernel-based classification algorithm for multi-label datasets (2016)
  10. Read, Jesse; Reutemann, Peter; Pfahringer, Bernhard; Holmes, Geoff: MEKA: a multi-label/multi-target extension to WEKA (2016)
  11. Spyromitros-Xioufis, Eleftherios; Tsoumakas, Grigorios; Groves, William; Vlahavas, Ioannis: Multi-target regression via input space expansion: treating targets as inputs (2016)
  12. Zhao, Shiwen; Gao, Chuan; Mukherjee, Sayan; Engelhardt, Barbara E.: Bayesian group factor analysis with structured sparsity (2016)
  13. Abe, Shigeo: Fuzzy support vector machines for multilabel classification (2015)
  14. Streich, Andreas P.; Buhmann, Joachim M.: Asymptotic analysis of estimators on multi-label data (2015)
  15. Cerri, Ricardo; Barros, Rodrigo C.; de Carvalho, André C. P. L. F.: Hierarchical multi-label classification using local neural networks (2014)
  16. Flores, M. Julia; Gámez, José A.; Martínez, Ana M.: Domains of competence of the semi-naive Bayesian network classifiers (2014)
  17. Lima, Ana Carolina E. S.; de Castro, Leandro Nunes: A multi-label, semi-supervised classification approach applied to personality prediction in social media (2014) ioport
  18. Liu, Huawen; Zhang, Shichao; Wu, Xindong: MLSLR: multilabel learning via sparse logistic regression (2014)
  19. Montañes, Elena; Senge, Robin; Barranquero, Jose; Quevedo, José Ramón; del Coz, Juan José; Hüllermeier, Eyke: Dependent binary relevance models for multi-label classification (2014) ioport
  20. Read, Jesse; Martino, Luca; Luengo, David: Efficient Monte Carlo methods for multi-dimensional learning with classifier chains (2014)

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