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Analyzing the Performance of a Multiobjective GA-P Algorithm for Learning Fuzzy Queries in a Machine Learning Environment

  • Oscar Cordón
  • Enrique Herrera-Viedma
  • María Luque
  • Félix de Moya
  • Carmen Zarco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2715)

Abstract

The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem, we find the Inductive Query by Example (IQBE) framework, where queries are automatically derived from sets of documents provided by the user. In this work we test the applicability of a multiobjective evolutionary IQBE technique for fuzzy queries in a machine learning environment. To do so, the Cranfield documentary collection is divided into two different document sets, labeled training and test, and the algorithm is run on the former to obtain several queries that are then validated on the latter.

Keywords

Pareto Front Relevant Document Relevance Feedback Information Retrieval System Boolean Query 
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 2003

Authors and Affiliations

  • Oscar Cordón
    • 1
  • Enrique Herrera-Viedma
    • 1
  • María Luque
    • 1
  • Félix de Moya
    • 2
  • Carmen Zarco
    • 3
  1. 1.Dept. of Computer Science and A.I.University of GranadaGranadaSpain
  2. 2.Dept. of Library and Information ScienceUniversity of GranadaGranadaSpain
  3. 3.PULEVA Food S.A.GranadaSpain

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