MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning
This tool addresses the need for a modular and flexible library for researchers and practitioners working on MIML learning problems, though it is incremental as it builds on existing methods without introducing new algorithms.
The authors developed MIML library, a Java software tool for multi-instance multi-label learning, which includes 43 algorithms and provides features like data management, validation methods, and performance evaluation metrics to facilitate algorithm development and comparison.
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.