MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning
Author
Belmonte Pérez, Álvaro Andrés
Zafra Gómez, Amelia
Gibaja, Eva
Publisher
ElsevierDate
2022Subject
Multi-instance learningMulti-label learning
Weka
Mulan
Classification
METS:
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Show full item recordAbstract
MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities for data managing and partitioning, holdout and cross-validation methods, standard metrics for performance evaluation, and generation of reports. In addition, algorithms can be executed through xml configuration files without needing to program. It is platform-independent, extensible, free, open-source, and available on GitHub under the GNU General Public License.