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Interesting regression- and model trees through variable restrictions
Department of Information Technology, University of Borås, Borås, Sweden.
Department of Information Technology, University of Borås, Borås, Sweden.ORCID iD: 0000-0003-0412-6199
Department of Information Technology, University of Borås, Borås, Sweden.
2015 (English)In: IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, SciTePress, 2015, 281-292 p.Conference paper, Published 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 M5s 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 evaluated against M5s 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
SciTePress, 2015. 281-292 p.
Keyword [en]
Golf, Interestingness, Model trees, Predictive modeling, Regression, Vehicle modeling, Automation, Automotive industry, Knowledge engineering, Knowledge management, Regression analysis, Sports, Vehicle model, Forestry
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-38118Scopus ID: 2-s2.0-84960873690ISBN: 9789897581588 (print)OAI: oai:DiVA.org:hj-38118DiVA: diva2:1163930
Conference
7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015, 12 November 2015 through 14 November 2015
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

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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
  • text
  • asciidoc
  • rtf