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Multi-Output Random Forests

The Random Forests ensemble predictor has proven to be well-suited for solving a multitudeof different prediction problems. In this thesis, we propose an extension to the Random Forestframework that allows Random Forests to be constructed for multi-output decision problemswith arbitrary combinations of classification and regression responses, with the goal ofincreasing predictive performance for such multi-output problems. We show that our methodfor combining decision tasks within the same decision tree reduces prediction error for mosttasks compared to single-output decision trees based on the same node impurity metrics, andprovide a comparison of different methods for combining such metrics. / Program: Magisterutbildning i informatik

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hb-17167
Date January 2013
CreatorsLinusson, Henrik
PublisherHögskolan i Borås, Institutionen Handels- och IT-högskolan, University of Borås/School of Business and IT
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationMagisteruppsats, ; 2013MAGI04

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