Qualitative radial basis function networks applied to financial credit risk prediction

X Rovira, N. Agell, N Sanchez, F Prats, X Parra

Research output: Indexed journal article Meeting Abstractpeer-review

Abstract

A methodology to use RBF when the descriptors of the patterns are given by means of ordinal variables is developed. A qualitative distance is constructed over the discrete structure of absolute orders of magnitude spaces. The aim is to capture the remoteness between the components of the patterns by locating labels with respect to extreme magnitudes. An application to a financial problem of the learning method described is given and permits a comparison of results obtained from a qualitative treatment with those from a quantitative treatment.
Original languageEnglish
Pages (from-to)111-118
Number of pages8
JournalFrontiers in Artificial Intelligence and Applications
Volume113
Publication statusPublished - 2004

Keywords

  • Learning algorithms
  • Orders of magnitude reasoning
  • Radial basis functions

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