4.6 Article

MIML library: A modular and flexible library for multi-instance multi-label learning

期刊

NEUROCOMPUTING
卷 500, 期 -, 页码 632-636

出版社

ELSEVIER
DOI: 10.1016/j.neucom.2022.05.068

关键词

Multi-instance learning; Multi-label learning; Weka; Mulan; Classification

资金

  1. Spanish Ministry of Science and Innovation [PID2020-115832 GB-I00]
  2. European Regional Development Fund

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The MIML library is a Java software tool for developing, testing, and comparing classification algorithms for MIML learning. It includes 43 algorithms, provides specific data management and partitioning formats, supports various evaluation methods, and allows algorithms to be executed through xml configuration files.
MIML library is a Java software tool to develop, test, and compare classification algorithms for multiinstance 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, opensource, and available on GitHub under the GNU General Public License.(c) 2022 Elsevier B.V. All rights reserved.

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