Retrieval based on self-explicative memories

Albert Fornells*, Eva Armengol, Elisabet Golobardes

*Corresponding author for this work

Research output: Book chapterConference contributionpeer-review

2 Citations (Scopus)

Abstract

One of the key issues in Case-Based Reasoning (CBR) systems is the efficient retrieval of cases when the case base is huge and/or it contains uncertainty and partial knowledge. We tackle these issues by organizing the case memory using an unsupervised clustering technique to identify data patterns for promoting all CBR steps. Moreover, another useful property of these patterns is that they provide to the user additional information about why the cases have been selected and retrieved through symbolic descriptions. This work analyses the introduction of this knowledge in the retrieve phase. The new strategies improve the case retrieval configuration procedure.

Original languageEnglish
Title of host publicationAdvances in Case-Based Reasoning - 9th European Conference, ECCBR 2008, Proceedings
Pages210-224
Number of pages15
DOIs
Publication statusPublished - 2008
Event9th European Conference on Case-Based Reasoning, ECCBR 2008 - Trier, Germany
Duration: 1 Sept 20084 Sept 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5239 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th European Conference on Case-Based Reasoning, ECCBR 2008
Country/TerritoryGermany
CityTrier
Period1/09/084/09/08

Keywords

  • Case memory organization
  • Case retrieval
  • Self-explicative memories
  • Self-organizing map
  • Soft case-based reasoning

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