Maintaining case-based reasoning systems using fuzzy decision trees1

Chi Keung Simon Shiu, Cai Hung Sun, Xi Zhao Wang, Daniel So Yeung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

23 Citations (Scopus)


This paper proposes a methodology of maintaining Case Based Reasoning (CBR) systems by using fuzzy decision tree induction - a machine learning technique. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which are generated by fuzzy decision trees. Firstly, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case-base. Secondly, clustering of cases will be carried out to identify different concepts in the case-base using the acquired feature knowledge. Thirdly, adaptation rules will be mined for each concept using fuzzy decision trees. Finally, a selection strategy based on the concepts of ε -coverage and ε -reachability is used to select representative cases. The effectiveness of the method is demonstrated experimentally using two sets of testing data.
Original languageEnglish
Title of host publicationAdvances in Case-Based Reasoning - 5th European Workshop, EWCBR 2000, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3540679332, 9783540679332
Publication statusPublished - 1 Jan 2000
Event5th European Workshop on Case-Based Reasoning, EWCBR 2000 - Trento, Italy
Duration: 6 Sept 20009 Sept 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th European Workshop on Case-Based Reasoning, EWCBR 2000

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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