Measuring the applicability of self-organization maps in a case-based reasoning system

A. Fornells, E. Golobardes, J. M. Martorell, J. M. Garrell, E. Bernadó, N. Macià

Research output: Book chapterConference contributionpeer-review

4 Citations (Scopus)

Abstract

Case-Based Reasoning (CBR) systems solve new problems using others which have been previously resolved. The knowledge is composed of a set of cases stored in a case memory, where each one describes a situation in terms of a set of features. Therefore, the size and organization of the case memory influences in the computational time needed to solve new situations. We organize the memory using Self-Organization Maps, which group cases with similar properties into patterns. Thus, CBR is able to do a selective retrieval using only the cases from the most suitable pattern. However, the data complexity may hinder the identification of patterns and it may degrade the accuracy rate. This work analyses the successful application of this approach by doing a previous data complexity characterization. Relationships between the performance and some measures of class separability and the discriminative power of attributes are also found.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - Third Iberian Conference, IbPRIA 2007, Proceedings
PublisherSpringer Verlag
Pages532-539
Number of pages8
EditionPART 2
ISBN (Print)9783540728481
DOIs
Publication statusPublished - 2007
Event3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007 - Girona, Spain
Duration: 6 Jun 20078 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4478 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2007
Country/TerritorySpain
CityGirona
Period6/06/078/06/07

Keywords

  • Case-based reasoning
  • Data complexity
  • Neural networks
  • Self-organization maps
  • Soft computing
  • Statistical and structural pattern recognition

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