MEKA

MEKA: A multi-label/multi-target extension to WEKA. Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-known WEKA library. MEKA provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. It supports multi-label and multi-target data, including in incremental and semi-supervised contexts.


References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
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  1. Do, Kien; Tran, Truyen; Nguyen, Thin; Venkatesh, Svetha: Attentional multilabel learning over graphs: a message passing approach (2019)
  2. Huang, Ming; Zhuang, Fuzhen; Zhang, Xiao; Ao, Xiang; Niu, Zhengyu; Zhang, Min-Ling; He, Qing: Supervised representation learning for multi-label classification (2019)
  3. Nguyen, Thi Thu Thuy; Nguyen, Tien Thanh; Sharma, Rabi; Liew, Alan Wee-Chung: A lossless online Bayesian classifier (2019)
  4. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  5. Adriano Rivolli; Andre C. P. L. F. de Carvalho: The utiml Package: Multi-label Classification in R (2018) not zbMATH
  6. 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
  7. Montiel, Jacob; Read, Jesse; Bifet, Albert; Abdessalem, Talel: Scikit-multiflow: a multi-output streaming framework (2018)
  8. Zhang, Yuanjian; Miao, Duoqian; Zhang, Zhifei; Xu, Jianfeng; Luo, Sheng: A three-way selective ensemble model for multi-label classification (2018)
  9. Huang, Kuan-Hao; Lin, Hsuan-Tien: Cost-sensitive label embedding for multi-label classification (2017)
  10. Piotr Szymanski: A scikit-based Python environment for performing multi-label classification (2017) arXiv
  11. Ghouti, Lahouari: A new kernel-based classification algorithm for multi-label datasets (2016)
  12. Read, Jesse; Reutemann, Peter; Pfahringer, Bernhard; Holmes, Geoff: MEKA: a multi-label/multi-target extension to WEKA (2016)