A4 Article in conference proceedings
Problem Transformation Methods with Distance-Based Learning for Multi-Target Regression (2020)

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

JYU authors or editors

Publication details

All authors or editors: Hämäläinen, Joonas; Kärkkäinen, Tommi

Parent publication: ESANN 2020 : Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference:

Place and date of conference: Bruges, Belgium (Online event), 2.-4.10.2020

eISBN: 978-2-87587-074-2

Publication year: 2020

Pages range: 691-696

Number of pages in the book: 726

Publisher: ESANN

Publication country: Belgium

Publication language: English

Persistent website address: https://www.esann.org/sites/default/files/proceedings/2020/ES2020-181.pdf

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/72475

Abstract

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.

Keywords: machine learning

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1