期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 39, 期 9, 页码 1811-1824出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2016.2618118
关键词
Transfer learning; model transfer; random forest; decision tree
资金
- Israel Science Foundation [1890/14, 920/12]
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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