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
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hb-17167 |
| Date | January 2013 |
| Creators | Linusson, Henrik |
| Publisher | Högskolan i Borås, Institutionen Handels- och IT-högskolan, University of Borås/School of Business and IT |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Magisteruppsats, ; 2013MAGI04 |
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