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DeepMTP : a Python-based deep learning framework for multi-target prediction

Dimitrios Iliadis (UGent) , Bernard De Baets (UGent) and Willem Waegeman (UGent)
(2023) SOFTWAREX. 23.
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Abstract
DeepMTP is a python framework designed to be compatible with the majority of machine learning sub-areas that fall under the umbrella of multi-target prediction (MTP). Multi-target prediction includes problem settings like multi-label classification, multivariate regression, multi-task learning, matrix completion, dyadic prediction, and zero-shot learning. Instead of using separate methodologies for the different problem settings, the proposed framework employs a single flexible two-branch neural network architecture that has been proven to be effective across the majority of MTP problem settings. To our knowledge, this is the first attempt at providing a framework that is compatible with more than two MTP problem settings. The source code of the framework is available at https://github.com/diliadis/DeepMTP and an extension with a graphical user-interface is available at https://github.com/diliadis/DeepMTP_gui
Keywords
Multi-target prediction, Multi-label classification, Multivariate regression, Multi-task learning

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MLA
Iliadis, Dimitrios, et al. “DeepMTP : A Python-Based Deep Learning Framework for Multi-Target Prediction.” SOFTWAREX, vol. 23, 2023, doi:10.1016/j.softx.2023.101516.
APA
Iliadis, D., De Baets, B., & Waegeman, W. (2023). DeepMTP : a Python-based deep learning framework for multi-target prediction. SOFTWAREX, 23. https://doi.org/10.1016/j.softx.2023.101516
Chicago author-date
Iliadis, Dimitrios, Bernard De Baets, and Willem Waegeman. 2023. “DeepMTP : A Python-Based Deep Learning Framework for Multi-Target Prediction.” SOFTWAREX 23. https://doi.org/10.1016/j.softx.2023.101516.
Chicago author-date (all authors)
Iliadis, Dimitrios, Bernard De Baets, and Willem Waegeman. 2023. “DeepMTP : A Python-Based Deep Learning Framework for Multi-Target Prediction.” SOFTWAREX 23. doi:10.1016/j.softx.2023.101516.
Vancouver
1.
Iliadis D, De Baets B, Waegeman W. DeepMTP : a Python-based deep learning framework for multi-target prediction. SOFTWAREX. 2023;23.
IEEE
[1]
D. Iliadis, B. De Baets, and W. Waegeman, “DeepMTP : a Python-based deep learning framework for multi-target prediction,” SOFTWAREX, vol. 23, 2023.
@article{01HA489TJY7BB58344F14KFVNR,
  abstract     = {{DeepMTP is a python framework designed to be compatible with the majority of machine learning sub-areas that fall under the umbrella of multi-target prediction (MTP). Multi-target prediction includes problem settings like multi-label classification, multivariate regression, multi-task learning, matrix completion, dyadic prediction, and zero-shot learning. Instead of using separate methodologies for the different problem settings, the proposed framework employs a single flexible two-branch neural network architecture that has been proven to be effective across the majority of MTP problem settings. To our knowledge, this is the first attempt at providing a framework that is compatible with more than two MTP problem settings. The source code of the framework is available at https://github.com/diliadis/DeepMTP and an extension with a graphical user-interface is available at https://github.com/diliadis/DeepMTP_gui}},
  articleno    = {{101516}},
  author       = {{Iliadis, Dimitrios and De Baets, Bernard and Waegeman, Willem}},
  issn         = {{2352-7110}},
  journal      = {{SOFTWAREX}},
  keywords     = {{Multi-target prediction,Multi-label classification,Multivariate regression,Multi-task learning}},
  language     = {{eng}},
  pages        = {{4}},
  title        = {{DeepMTP : a Python-based deep learning framework for multi-target prediction}},
  url          = {{http://doi.org/10.1016/j.softx.2023.101516}},
  volume       = {{23}},
  year         = {{2023}},
}
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