MEKA: A Multi-label/Multi-target Extension to WEKA
Authors: Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes
JMLR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Meka provides interfaces to facilitate practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi-label experiments and development. (...) Meka is noticeably faster than Mulan in some implementations under the same configurations, for example RAk EL, see Table 2. |
| Researcher Affiliation | Academia | Jesse Read EMAIL Helsinki Institute for Information Technology (HIIT), and Aalto University, Dept. of Computer Science, Espoo, Finland Peter Reutemann EMAIL Bernhard Pfahringer EMAIL GeoffHolmes EMAIL Department of Computer Science University of Waikato, New Zealand |
| Pseudocode | No | The paper describes the Meka framework, its features, and how it can be used, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Meka is released under the GNU GPL 3 licence; can run on any Java (1.7 or above) machine. The software with source code, API reference, data sets, list of methods with examples, and a tutorial can be found at at http://meka.sourceforge.net |
| Open Datasets | Yes | For example, to run five fold cross validation of an ensemble of 50 chain classifiers (Read et al., 2011) on the Music data set (...) Table 2: Running time (s) of RAk EL under Meka and Mulan, with SMO, k = 3, m = 10. (referring to Enron, Corel-5k, Media Mill datasets) (...) The software with source code, API reference, data sets, list of methods with examples, and a tutorial can be found at at http://meka.sourceforge.net |
| Dataset Splits | Yes | For example, to run five fold cross validation of an ensemble of 50 chain classifiers (Read et al., 2011) on the Music data set with support vector machines as the base classifier (as also shown on the right of Figure 1): java meka.classifiers.multilabel.meta.Bagging ML -x 5 -t data/Music.arff -I 50 \ -W meka.classifiers.multilabel.CC --W weka.classifiers.trees.SMO |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | Yes | Meka can be run on any machine with Java installed (version 1.7 or above). |
| Experiment Setup | Yes | For example, to run five fold cross validation of an ensemble of 50 chain classifiers (Read et al., 2011) on the Music data set with support vector machines as the base classifier (as also shown on the right of Figure 1): java meka.classifiers.multilabel.meta.Bagging ML -x 5 -t data/Music.arff -I 50 \ -W meka.classifiers.multilabel.CC --W weka.classifiers.trees.SMO |