New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation

Tomasz Kajdanowicz, Slawomir Plamowski and Przemyslaw Kazienko
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Tomasz Kajdanowicz: Wroclaw University of Technology, Poland
Slawomir Plamowski: Wroclaw University of Technology, Poland
Przemyslaw Kazienko: Wroclaw University of Technology, Poland

International Journal of Information Technology and Web Engineering (IJITWE), 2012, vol. 7, issue 2, 60-69

Abstract: Choosing a proper training set for machine learning tasks is of great importance in complex domain problems. In the paper a new distance measure for training set selection is presented and thoroughly discussed. The distance between two datasets is computed using variance of entropy in groups obtained after clustering. The approach is validated using real domain datasets from debt portfolio valuation process. Eventually, prediction performance is examined.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jitwe0:v:7:y:2012:i:2:p:60-69

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