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Multi-Output Random Forests
University of Borås, School of Business and IT.
2013 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

The Random Forests ensemble predictor has proven to be well-suited for solving a multitude of different prediction problems. In this thesis, we propose an extension to the Random Forest framework that allows Random Forests to be constructed for multi-output decision problems with arbitrary combinations of classification and regression responses, with the goal of increasing predictive performance for such multi-output problems. We show that our method for combining decision tasks within the same decision tree reduces prediction error for most tasks compared to single-output decision trees based on the same node impurity metrics, and provide a comparison of different methods for combining such metrics.

Place, publisher, year, edition, pages
University of Borås/School of Business and IT , 2013.
Series
Magisteruppsats ; 2013MAGI04
Keywords [en]
classification, multi-output, multi-task, Random Forest, regression
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hb:diva-17167Local ID: 2320/12407OAI: oai:DiVA.org:hb-17167DiVA, id: diva2:1309070
Note
Program: Magisterutbildning i informatikAvailable from: 2019-04-30 Created: 2019-04-30

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CiteExportLink to record
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Citation style
  • apa
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Output format
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