Distance measures in training set selection for debt value prediction

Authors:
Tomasz Kajdanowicz;Slawomir Plamowski;Przemyslaw Kazienko
Affiliations:
Faculty of Computer Science and Management, Wroclaw University of Technology, Wroclaw, Poland;Faculty of Computer Science and Management, Wroclaw University of Technology, Wroclaw, Poland;Faculty of Computer Science and Management, Wroclaw University of Technology, Wroclaw, Poland
Venue:
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Year:
2012

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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.