Abstract
A comparative study over six learning scenarios in debt pattern recognition is presented in the paper. There are proposed new approaches for distance measure definitions in training set selection. Using those measures for training set selection the inference models are trained using distinct reference. All proposed approaches are examined in dataset selection during prediction of debt portfolio value. Finally, basic evaluation on prediction performance is conducted.
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Kajdanowicz, T., Plamowski, S., Kazienko, P. (2012). Distance Measures in Training Set Selection for Debt Value Prediction. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_28
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DOI: https://doi.org/10.1007/978-3-642-27387-2_28
Publisher Name: Springer, Berlin, Heidelberg
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