A connectionist approach to producing rules describing monthly UK Divisia data

Vincent A. Schmidt, Jane M. Binner

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

This paper demonstrates a mechanism, whereby rules can be extracted from a feedforward neural network trained to characterize the money-price relationship, defined as the relationship between the rate of growth of the money supply and inflation. Monthly Divisia component data is encoded and used to train a group of candidate connectionist architectures. One candidate is selected for rule extraction, using a custom decompositional extraction algorithm that generates rules in human-readable and machine-executable form. Rule and network accuracy are compared, and comments are made on the relationships expressed within the discovered rules. The types of discovered relationships could be used to guide monetary policy decisions.

Original languageEnglish
Title of host publicationProceedings of the 2008 International Conference on Artificial Intelligence, ICAI 2008 and Proceedings of the 2008 International Conference on Machine Learning; Models, Technologies and Applications
Pages468-474
Number of pages7
Publication statusPublished - 1 Dec 2008
Event2008 International Conference on Artificial Intelligence, ICAI 2008 and 2008 International Conference on Machine Learning; Models, Technologies and Applications, MLMTA 2008 - Las Vegas, NV, United Kingdom
Duration: 14 Jul 200817 Jul 2008

Conference

Conference2008 International Conference on Artificial Intelligence, ICAI 2008 and 2008 International Conference on Machine Learning; Models, Technologies and Applications, MLMTA 2008
CountryUnited Kingdom
CityLas Vegas, NV
Period14/07/0817/07/08

Keywords

  • Data mining
  • Divisia
  • Inflation
  • Neural Network
  • Rule generation

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