Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression
Hämäläinen, J., & Kärkkäinen, T. (2020). Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression. In ESANN 2020 : Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 691-696). ESANN. https://www.esann.org/sites/default/files/proceedings/2020/ES2020-181.pdf
Date
2020Copyright
© The Authors, 2019
Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods, the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM), in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential, emphasizing the utility of the problem transformation especially with the EMLM.
Publisher
ESANNParent publication ISBN
978-2-87587-074-2Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningIs part of publication
ESANN 2020 : Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine LearningKeywords
Multi-target regression is a special subset of supervised machine learning problems. Problem transformation methods are used in the field to improve the performance of basic methods. The purpose of this article is to test the use of recently popularized distance-based methods the minimal learning machine (MLM) and the extreme minimal learning machine (EMLM) in problem transformation. The main advantage of the full data variants of these methods is the lack of any meta-parameter. The experimental results for the MLM and EMLM show promising potential emphasizing the utility of the problem transformation especially with the EMLM. koneoppiminen
Original source
https://www.esann.org/sites/default/files/proceedings/2020/ES2020-181.pdfPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/43492634
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Research Council of FinlandFunding program(s)
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