MULAN

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.

Keywords for this software

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References in zbMATH (referenced in 65 articles )

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  1. Bidgoli, Azam Asilian; Ebrahimpour-Komleh, Hossein; Rahnamayan, Shahryar: Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection (2021)
  2. Huang, Jun; Xu, Linchuan; Qian, Kun; Wang, Jing; Yamanishi, Kenji: Multi-label learning with missing and completely unobserved labels (2021)
  3. Nguyen, Vu-Linh; Hüllermeier, Eyke: Multilabel classification with partial abstention: Bayes-optimal prediction under label independence (2021)
  4. Che, Xiaoya; Chen, Degang; Mi, Jusheng: A novel approach for learning label correlation with application to feature selection of multi-label data (2020)
  5. Jeong, Jun-Yong; Kang, Ju-Seok; Jun, Chi-Hyuck: Regularization-based model tree for multi-output regression (2020)
  6. Kocev, Dragi; Ceci, Michelangelo; Stepišnik, Tomaž: Ensembles of extremely randomized predictive clustering trees for predicting structured outputs (2020)
  7. Rivolli, Adriano; Read, Jesse; Soares, Carlos; Pfahringer, Bernhard; de Carvalho, André C. P. L. F.: An empirical analysis of binary transformation strategies and base algorithms for multi-label learning (2020)
  8. Slawski, Martin; Ben-David, Emanuel; Li, Ping: Two-stage approach to multivariate linear regression with sparsely mismatched data (2020)
  9. Tan, Zhi-Hao; Tan, Peng; Jiang, Yuan; Zhou, Zhi-Hua: Multi-label optimal margin distribution machine (2020)
  10. Wu, Guoqiang; Zheng, Ruobing; Tian, Yingjie; Liu, Dalian: Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification (2020)
  11. Yang, Bo; Tong, Kunkun; Zhao, Xueqing; Pang, Shanmin; Chen, Jinguang: Multilabel classification using low-rank decomposition (2020)
  12. Chu, Hong-Min; Huang, Kuan-Hao; Lin, Hsuan-Tien: Dynamic principal projection for cost-sensitive online multi-label classification (2019)
  13. Do, Kien; Tran, Truyen; Nguyen, Thin; Venkatesh, Svetha: Attentional multilabel learning over graphs: a message passing approach (2019)
  14. Huang, Jun; Qin, Feng; Zheng, Xiao; Cheng, Zekai; Yuan, Zhixiang; Zhang, Weigang; Huang, Qingming: Improving multi-label classification with missing labels by learning label-specific features (2019)
  15. Huang, Ming; Zhuang, Fuzhen; Zhang, Xiao; Ao, Xiang; Niu, Zhengyu; Zhang, Min-Ling; He, Qing: Supervised representation learning for multi-label classification (2019)
  16. Adriano Rivolli; Andre C. P. L. F. de Carvalho: The utiml Package: Multi-label Classification in R (2018) not zbMATH
  17. 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
  18. Li, Gen; Gaynanova, Irina: A general framework for association analysis of heterogeneous data (2018)
  19. Ma, Jianghong; Chow, Tommy W. S.: Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels (2018)
  20. Pliakos, Konstantinos; Geurts, Pierre; Vens, Celine: Global multi-output decision trees for interaction prediction (2018)

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