Dynamic rule activation for Extended Belief Rule Bases

Alberto Calzada, Jun Liu, Hui Wang, Anil Kashyap

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

8 Citations (Scopus)

Abstract

Incompleteness and inconsistent situations are common in most rule-based decision support systems (DSS). However, most rule inference methods do not provide procedures to specifically tackle and/or analyze them. This research presents a single approach for both incompleteness and inconsistency issues with a simple yet effective method. During the rule activation step, data incompleteness and inconsistency may be seen as paired situations, since the former appears due to lack of information while the latter can be represented as an excess of heterogeneous information activated. To effectively take advantage of this fact, this research presents a Dynamic Rule Activation (DRA) method, which searches for a balance between both incomplete and inconsistent situations to improve the overall performance of the DSS. Although DRA is designed as a flexible method, able to work with most similarity measures, in this research it is applied in the context of Extended Belief Rule-Bases (E-BRBs). The case studies illustrated in this research demonstrate how the use of DRA can improve the accuracy of E-BRB based decision support models. In this regard, the RIMER+ model and the simple weighted average of the activated rules were tested with and without using DRA as pre-processing method.
Original languageEnglish
Title of host publication 2013 International Conference on Machine Learning and Cybernetics: Proceedings
Place of PublicationUnited States
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1836-1841
Number of pages6
DOIs
Publication statusPublished - 08 Sept 2014
Externally publishedYes

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
PublisherIEEE Computer Society
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Bibliographical note

Publisher Copyright: © 2013 IEEE.; 12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 ; Conference date: 14-07-2013 Through 17-07-2013

Keywords

  • belief rule-base
  • Decision making
  • decision support system
  • information incompleteness
  • spatial decision making
  • uncertainty
  • urban regeneration

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