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 28 articles )

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  1. Arias, Jacinto; Gamez, Jose A.; Nielsen, Thomas D.; Puerta, Jose M.: A scalable pairwise class interaction framework for multidimensional classification (2016)
  2. Loza Mencía, Eneldo; Janssen, Frederik: Learning rules for multi-label classification: a stacking and a separate-and-conquer approach (2016)
  3. Read, Jesse; Reutemann, Peter; Pfahringer, Bernhard; Holmes, Geoff: MEKA: A multi-label/multi-target extension to WEKA (2016)
  4. Streich, Andreas P.; Buhmann, Joachim M.: Asymptotic analysis of estimators on multi-label data (2015)
  5. Cerri, Ricardo; Barros, Rodrigo C.; de Carvalho, André C.P.L.F.: Hierarchical multi-label classification using local neural networks (2014)
  6. Flores, M.Julia; Gámez, José A.; Martínez, Ana M.: Domains of competence of the semi-naive Bayesian network classifiers (2014)
  7. Lima, Ana Carolina E.S.; de Castro, Leandro Nunes: A multi-label, semi-supervised classification approach applied to personality prediction in social media (2014)
  8. Montañes, Elena; Senge, Robin; Barranquero, Jose; Quevedo, José Ramón; del Coz, Juan José; Hüllermeier, Eyke: Dependent binary relevance models for multi-label classification (2014)
  9. Read, Jesse; Martino, Luca; Luengo, David: Efficient Monte Carlo methods for multi-dimensional learning with classifier chains (2014)
  10. Xu, Jianhua: Multi-label core vector machine with a zero label (2014)
  11. Chekina, Lena; Gutfreund, Dan; Kontorovich, Aryeh; Rokach, Lior; Shapira, Bracha: Exploiting label dependencies for improved sample complexity (2013)
  12. Kumar, Abhishek; Vembu, Shankar; Menon, Aditya Krishna; Elkan, Charles: Beam search algorithms for multilabel learning (2013)
  13. Liu, Huawen; Zheng, Zhonglong; Zhao, Jianmin; Ye, Ronghua: An ensemble method for high-dimensional multilabel data (2013)
  14. Pillai, Ignazio; Fumera, Giorgio; Roli, Fabio: Multi-label classification with a reject option (2013)
  15. Pillai, Ignazio; Fumera, Giorgio; Roli, Fabio: Threshold optimisation for multi-label classifiers (2013)
  16. Yu, Ying; Pedrycz, Witold; Miao, Duoqian: Neighborhood rough sets based multi-label classification for automatic image annotation (2013)
  17. Dembczyński, Krzysztof; Waegeman, Willem; Cheng, Weiwei; Hüllermeier, Eyke: On label dependence and loss minimization in multi-label classification (2012)
  18. Madjarov, Gjorgji; Kocev, Dragi; Gjorgjevikj, Dejan; Džeroski, Sašo: An extensive experimental comparison of methods for multi-label learning (2012)
  19. Miao, Xu; Rao, Rajesh P.N.: Fast structured prediction using large margin sigmoid belief networks (2012)
  20. Quevedo, José Ramón; Luaces, Oscar; Bahamonde, Antonio: Multilabel classifiers with a probabilistic thresholding strategy (2012)

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