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 )

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  1. Iliadis, Dimitrios; De Baets, Bernard; Waegeman, Willem: Multi-target prediction for dummies using two-branch neural networks (2022)
  2. Xia, Yuelong; Chen, Ke; Yang, Yun: Multi-label classification with weighted classifier selection and stacked ensemble (2021)
  3. Do, Kien; Tran, Truyen; Nguyen, Thin; Venkatesh, Svetha: Attentional multilabel learning over graphs: a message passing approach (2019)
  4. Huang, Ming; Zhuang, Fuzhen; Zhang, Xiao; Ao, Xiang; Niu, Zhengyu; Zhang, Min-Ling; He, Qing: Supervised representation learning for multi-label classification (2019)
  5. Nguyen, Thi Thu Thuy; Nguyen, Tien Thanh; Sharma, Rabi; Liew, Alan Wee-Chung: A lossless online Bayesian classifier (2019)
  6. Adriano Rivolli; Andre C. P. L. F. de Carvalho: The utiml Package: Multi-label Classification in R (2018) not zbMATH
  7. Francisco Charte, Antonio J. Rivera, David Charte, MarĂ­a 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
  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)