Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests
This work addresses model adaptation for domain variations in machine learning, presenting an incremental improvement in transfer learning techniques.
The paper tackles the problem of transferring decision forest models from a source domain to a target domain by proposing two algorithms: one modifies tree structures greedily, and the other adjusts decision node thresholds, with an ensemble combining both. The methods show impressive experimental results across various problems.
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.