DeepMTP: A Python']Python-based deep learning framework for multi-target prediction

被引:1
|
作者
Iliadis, Dimitrios [1 ]
De Baets, Bernard [1 ]
Waegeman, Willem [1 ]
机构
[1] Univ Ghent, Dept Data Anal & Math Modeling, KERMIT, Coupure Links 653, B-9000 Ghent, Belgium
关键词
Multi-target prediction; Multi-label classification; Multivariate regression; Multi-task learning;
D O I
10.1016/j.softx.2023.101516
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
DeepMTP is a python framework designed to be compatible with the majority of machine learning subareas 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.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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页数:4
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