text: We propose novel model transfer-learning methods that refine a decision forest model @math 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.
  @cite_31: Most research in machine learning focuses on scenarios in which a learner faces a single learning task, independently of other learning tasks or prior knowledge. In reality, however, learning is not performed in isolation, starting from scratch with every new task. Instead, it is a lifelong activity during which a learner encounters many learning tasks, and usefully transfers to new tasks knowledge acquired from earlier related tasks. We propose a novel approach to transfer learning with decision trees. Our system learns a new task semi-incrementally from a partial decision tree model which captures knowledge from a previous task. Empirical results on several UCI data sets show that our approach is generally more effective and accurate than the base approach.
summary: Most early transfer learning methods were based on neural networks, and while SVMs and ensemble techniques have become prominent in this field, DT models are still under-explored in this setting. Of the few tree-based techniques researched, only the work by operates in our model transfer setting. Specifically, proposed a simple technique to update an existing tree trained only on source samples using target samples @cite_31 . Their approach resembled a batch iterative learning technique which relies solely on iterative expansion steps. This technique does not consider any refitting of numeric feature thresholds.