Multi-Output Random Forests

• Main Author: Linusson, Henrik
• Format: Others
• Language: English
• Published: Högskolan i Borås, Institutionen Handels- och IT-högskolan 2013
• Subjects: classification; multi-output; multi-task; Random Forest; regression; Engineering and Technology; Teknik och teknologier
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-17167

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