MEKA: A Multi-label/Multi-target Extension to WEKA

By Jesse Read, Peter Reutemann, Bernhard Pfahringer, G. Holmes. 2016

239 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Radwa El Shawi
2. Jesse Read
3. Jacob Montiel
4. A. D. Sá
5. Alex Alves Freitas
6. Mohamed Maher
7. Sherif Sakr
8. Albert Bifet

Citations in the corpus

• Automated Machine Learning: State-of-The-Art and Open Challenges (2019, background): “Auto − MEKAGGP takes as input both of the dataset and a grammar describing the hierarchical search space of the hyper-parameters and the learning algorithms from MEKA(8) framework [102].”
• Automated Selection and Configuration of Multi-Label Classification Algorithms with Grammar-Based Genetic Programming (2018, methodology): “This section presents Automated MEKA (Auto-MEKAGGP), a method conceived to automatically select and configure MLC algorithms in the MEKA tool [13].”
• Scikit-Multiflow: A Multi-output Streaming Framework (2018, methodology): “The MEKA project (Read et al., 2016) provides an open source implementation of methods for multi-label learning and evaluation.”