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Subgroup Discovery for Weight Learning in Breast Cancer Diagnosis

  • Beatriz López
  • Víctor Barrera
  • Joaquim Meléndez
  • Carles Pous
  • Joan Brunet
  • Judith Sanz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)

Abstract

In the recent years, there is an increasing interest of the use of case-based reasoning (CBR) in medicine. CBR is an approach to problem solving that is able to use specific knowledge of previous experiences. However, the efficiency of CBR strongly depends on the similarity metrics used to recover past experiences. In such metrics, the role of attribute weights is critical. In this paper we propose a methodology that use subgroup discovery methods to learn the relevance of the attributes. The methodology is applied to a Breast Cancer dataset obtaining significant improvements. ...

Keywords

Breast Cancer Diagnosis Attribute Weight Subgroup Discovery Breast Cancer Dataset Weight Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Beatriz López
    • 1
  • Víctor Barrera
    • 1
  • Joaquim Meléndez
    • 1
  • Carles Pous
    • 1
  • Joan Brunet
    • 2
  • Judith Sanz
    • 3
  1. 1.Institut d’Informática i AplicacionsUniversitat de GironaGironaSpain
  2. 2.Institut d’Investigació Biomèdica de Girona and Institut Català d’OncologiaGironaSpain
  3. 3.Hospital Sant PauBarcelonaSpain

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