Decision Trees for Instance Transfer

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Instance-transfer learning has emerged as a promising learning framework to boost performance of predictive models for a target domain by exploiting data from source domains. The success of the framework depends on the relevance of the source data to the target data. This paper proposes a decision-tree approach for instance transfer when the source and target data are relevant with respect to a strict subset of input features. Experimental results on real-world data sets demonstrate that the proposed approach outperforms existing instance-transfer approaches when the source and target data are partially related.
Original languageEnglish
Title of host publicationConformal and Probabilistic Prediction with Applications. COPA 2016
EditorsA. Gammerman, Z. Luo, J. Vega, V. Vovk
PublisherSpringer, Cham
ISBN (Electronic)978-3-319-33395-3
ISBN (Print)978-3-319-33394-6
Publication statusPublished - 2016

Publication series

SeriesLecture Notes in Computer Science

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