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Interesting Regression- and Model Trees Through Variable Restrictions
University of Borås, Faculty of Librarianship, Information, Education and IT. (CSL@BS)
University of Borås, Faculty of Librarianship, Information, Education and IT. (CSL@BS)
Scania CV AB.
University of Borås, Faculty of Librarianship, Information, Education and IT. (CSL@BS)
2015 (English)In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015, 281-292 p.Conference paper, (Refereed)
Abstract [en]

The overall purpose of this paper is to suggest a new technique for creating interesting regression- and model trees. Interesting models are here defined as models that fulfill some domain dependent restriction of how variables can be used in the models. The suggested technique, named ReReM, is an extension of M5 which can enforce variable constraints while creating regression and model trees. To evaluate ReReM, two case studies were conducted where the first concerned modeling of golf player skill, and the second modeling of fuel consumption in trucks. Both case studies had variable constraints, defined by domain experts, that should be fulfilled for models to be deemed interesting. When used for modeling golf player skill, ReReM created regression trees that were slightly less accurate than M5’s regression trees. However, the models created with ReReM were deemed to be interesting by a golf teaching professional while the M5 models were not. In the second case study, ReReM was evalu ated against M5’s model trees and a semi-automated approach often used in the automotive industry. Here, experiments showed that ReReM could achieve a predictive performance comparable to M5 and clearly better than a semi-automated approach, while fulfilling the constraints regarding interesting models.

Place, publisher, year, edition, pages
2015. 281-292 p.
Keyword [en]
Predictive Modeling, Model Trees, Interestingness, Regression, Vehicle modeling, Golf
National Category
Computer Science
Research subject
Bussiness and IT
Identifiers
URN: urn:nbn:se:hb:diva-8847DOI: 10.5220/0005600302810292ISBN: 978-989-758-158-8 (print)OAI: oai:DiVA.org:hb-8847DiVA: diva2:903029
Conference
International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Projects
Big Data Analytics by OnlineEnsemble Learning (BOEL)Golf data analysis (GOATS)
Funder
Knowledge Foundation, 20120192
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2017-05-02

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Rikard, KönigUlf, JohanssonPeter, Brattberg
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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