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

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

Tomasz Kajdanowicz, Slawomir Plamowski, Przemyslaw Kazienko
Copyright: © 2012 |Volume: 7 |Issue: 2 |Pages: 10
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781466612921|DOI: 10.4018/jitwe.2012040105
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MLA

Kajdanowicz, Tomasz, et al. "New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation." IJITWE vol.7, no.2 2012: pp.60-69. http://doi.org/10.4018/jitwe.2012040105

APA

Kajdanowicz, T., Plamowski, S., & Kazienko, P. (2012). New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation. International Journal of Information Technology and Web Engineering (IJITWE), 7(2), 60-69. http://doi.org/10.4018/jitwe.2012040105

Chicago

Kajdanowicz, Tomasz, Slawomir Plamowski, and Przemyslaw Kazienko. "New Entropy Based Distance for Training Set Selection in Debt Portfolio Valuation," International Journal of Information Technology and Web Engineering (IJITWE) 7, no.2: 60-69. http://doi.org/10.4018/jitwe.2012040105

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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.

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