Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 39, Issue 9, Pages 1811-1824Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2016.2618118
Keywords
Transfer learning; model transfer; random forest; decision tree
Funding
- Israel Science Foundation [1890/14, 920/12]
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We propose novel model transfer-learning methods that refine a decision forest model M learned within a source domain using a training set sampled from a target domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes. The second algorithm does not modify structure, but only the parameter (thresholds) associated with decision nodes. We also propose to combine both methods by considering an ensemble that contains the union of the two forests. The proposed methods exhibit impressive experimental results over a range of problems.
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