Transfer Learning in Decision Trees

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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.
Jun Won Lee, Christophe G. Giraud-Carrier
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Authors Jun Won Lee, Christophe G. Giraud-Carrier
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