Transfer Learning in Decision Trees

— 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.

• Type: Conference
• Year: 2007
• Where: IJCNN
• Authors: Jun Won Lee, Christophe G. Giraud-Carrier
• Keywords: IJCNN 2007, Learning Tasks, Machine Learning Focuses, Single Learning Task