Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Decision forest models are powerful but can be domain-specific.
• Adapting models to new data variations (target domains) is challenging.
• Transfer learning offers a promising approach to domain adaptation.

Purpose of the Study:

• To propose novel transfer learning methods for refining decision forest models.
• To adapt a source domain model (M) to a related target domain.
• To improve model performance on varied datasets.

Main Methods:

• Developed two random forest transfer learning algorithms.
• Algorithm 1: Greedily modifies tree structures locally (expansion/reduction).
• Algorithm 2: Modifies decision node parameters (thresholds) without changing structure.
• Proposed an ensemble combining both forest modifications.

Main Results:

• The proposed transfer learning methods demonstrated impressive experimental results.
• Significant performance improvements were observed across a range of problems.
• Both individual algorithms and the combined ensemble showed effectiveness.

Conclusions:

• Novel transfer learning approaches effectively refine decision forest models for domain adaptation.
• Methods offer flexible strategies for adapting models by adjusting structure or parameters.
• The ensemble approach provides a robust way to leverage both adaptation strategies.