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Transfer Learning Approach to Debt Portfolio Appraisal

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7209)

Abstract

Machine learning and data mining algorithms usually assume that the training and future data have the same distribution and come from the same feature space. However, in majority of real-world problems, this is not true. In case of Debt portfolio appraisal we have sufficient training data only in another domain of interest, namely in other portfolios. Therefore, only knowledge transfer from these portfolios in inference for new one is possible. In the paper we propose transfer learning and learning based on similarity methods, basing on similarity between training and testing datasets. The proposed approach is examined in real domain debt portfolio valuation.

Keywords

  • Transfer learning
  • dataset selection
  • distance measures
  • debt valuation
  • prediction
  • supervised learning

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© 2012 Springer-Verlag Berlin Heidelberg

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Kajdanowicz, T., Plamowski, S., Kazienko, P., Indyk, W. (2012). Transfer Learning Approach to Debt Portfolio Appraisal. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28931-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-28931-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28930-9

  • Online ISBN: 978-3-642-28931-6

  • eBook Packages: Computer ScienceComputer Science (R0)