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Multiple Instance Learning with Genetic Programming for Web Mining

  • A. Zafra
  • S. Ventura
  • E. Herrera-Viedma
  • C. Romero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

Abstract

The aim of this paper is to present a new tool of multiple instance learning which is designed using a grammar based genetic programming (GGP) algorithm. We study its application in Web Mining framework to identify web pages interesting for the users. This new tool called GGP-MI algorithm is evaluated and compared with other available algorithms which extend a well-known neighborhood based algorithm (k-nearest neighbour algorithm) to multiple instance learning. Computational experiments show that, the GGP-MI algorithm obtains competitive results, solves problems of other algorithms, such as sparsity and scalability and adds comprehensibility and clarity in the knowledge discovery process.

Keywords

Multiple Instance Multiple Instance Learn Lazy Learning Knowledge Discovery Process Grammar Base Genetic Programming 
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 2007

Authors and Affiliations

  • A. Zafra
    • 1
  • S. Ventura
    • 2
  • E. Herrera-Viedma
    • 1
  • C. Romero
    • 2
  1. 1.Department of Computer Science and Artificial Intelligence. University of Granada 
  2. 2.Department of Computer Science and Numerical Analysis. University of Córdoba 

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