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 56 articles )

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  1. Tan, Zhi-Hao; Tan, Peng; Jiang, Yuan; Zhou, Zhi-Hua: Multi-label optimal margin distribution machine (2020)
  2. Wu, Guoqiang; Zheng, Ruobing; Tian, Yingjie; Liu, Dalian: Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification (2020)
  3. Chu, Hong-Min; Huang, Kuan-Hao; Lin, Hsuan-Tien: Dynamic principal projection for cost-sensitive online multi-label classification (2019)
  4. Do, Kien; Tran, Truyen; Nguyen, Thin; Venkatesh, Svetha: Attentional multilabel learning over graphs: a message passing approach (2019)
  5. Huang, Ming; Zhuang, Fuzhen; Zhang, Xiao; Ao, Xiang; Niu, Zhengyu; Zhang, Min-Ling; He, Qing: Supervised representation learning for multi-label classification (2019)
  6. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  7. Adriano Rivolli; Andre C. P. L. F. de Carvalho: The utiml Package: Multi-label Classification in R (2018) not zbMATH
  8. 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
  9. Li, Gen; Gaynanova, Irina: A general framework for association analysis of heterogeneous data (2018)
  10. Ma, Jianghong; Chow, Tommy W. S.: Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels (2018)
  11. Pliakos, Konstantinos; Geurts, Pierre; Vens, Celine: Global multi-output decision trees for interaction prediction (2018)
  12. Wei, Tong; Guo, Lan-Zhe; Li, Yu-Feng; Gao, Wei: Learning safe multi-label prediction for weakly labeled data (2018)
  13. Yang, Zhuoran; Ning, Yang; Liu, Han: On semiparametric exponential family graphical models (2018)
  14. Zhang, Yuanjian; Miao, Duoqian; Zhang, Zhifei; Xu, Jianfeng; Luo, Sheng: A three-way selective ensemble model for multi-label classification (2018)
  15. Huang, Kuan-Hao; Lin, Hsuan-Tien: Cost-sensitive label embedding for multi-label classification (2017)
  16. Liu, Weiwei; Tsang, Ivor W.: Making decision trees feasible in ultrahigh feature and label dimensions (2017)
  17. Liu, Weiwei; Tsang, Ivor W.; Müller, Klaus-Robert: An easy-to-hard learning paradigm for multiple classes and multiple labels (2017)
  18. Melki, Gabriella; Cano, Alberto; Kecman, Vojislav; Ventura, Sebastián: Multi-target support vector regression via correlation regressor chains (2017)
  19. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  20. Tao, Chenyang; Feng, Jianfeng: Canonical kernel dimension reduction (2017)

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