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 32 articles )

Showing results 1 to 20 of 32.
Sorted by year (citations)

1 2 next

  1. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  2. Wu, Yu-Ping; Lin, Hsuan-Tien: Progressive random $k$-labelsets for cost-sensitive multi-label classification (2017)
  3. Arias, Jacinto; Gamez, Jose A.; Nielsen, Thomas D.; Puerta, Jose M.: A scalable pairwise class interaction framework for multidimensional classification (2016)
  4. Loza Mencía, Eneldo; Janssen, Frederik: Learning rules for multi-label classification: a stacking and a separate-and-conquer approach (2016)
  5. Read, Jesse; Reutemann, Peter; Pfahringer, Bernhard; Holmes, Geoff: MEKA: a multi-label/multi-target extension to WEKA (2016)
  6. Zhao, Shiwen; Gao, Chuan; Mukherjee, Sayan; Engelhardt, Barbara E.: Bayesian group factor analysis with structured sparsity (2016)
  7. Streich, Andreas P.; Buhmann, Joachim M.: Asymptotic analysis of estimators on multi-label data (2015)
  8. Cerri, Ricardo; Barros, Rodrigo C.; de Carvalho, André C.P.L.F.: Hierarchical multi-label classification using local neural networks (2014)
  9. Flores, M.Julia; Gámez, José A.; Martínez, Ana M.: Domains of competence of the semi-naive Bayesian network classifiers (2014)
  10. 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
  11. Liu, Huawen; Zhang, Shichao; Wu, Xindong: MLSLR: multilabel learning via sparse logistic regression (2014)
  12. 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
  13. Read, Jesse; Martino, Luca; Luengo, David: Efficient Monte Carlo methods for multi-dimensional learning with classifier chains (2014)
  14. Xu, Jianhua: Multi-label core vector machine with a zero label (2014)
  15. Chekina, Lena; Gutfreund, Dan; Kontorovich, Aryeh; Rokach, Lior; Shapira, Bracha: Exploiting label dependencies for improved sample complexity (2013)
  16. Kumar, Abhishek; Vembu, Shankar; Menon, Aditya Krishna; Elkan, Charles: Beam search algorithms for multilabel learning (2013)
  17. Liu, Huawen; Zheng, Zhonglong; Zhao, Jianmin; Ye, Ronghua: An ensemble method for high-dimensional multilabel data (2013)
  18. Pillai, Ignazio; Fumera, Giorgio; Roli, Fabio: Multi-label classification with a reject option (2013) ioport
  19. Pillai, Ignazio; Fumera, Giorgio; Roli, Fabio: Threshold optimisation for multi-label classifiers (2013)
  20. Yu, Ying; Pedrycz, Witold; Miao, Duoqian: Neighborhood rough sets based multi-label classification for automatic image annotation (2013)

1 2 next