Advertisement

Using Genetic Algorithms to Improve Accuracy of Economical Indexes Prediction

  • Óscar Cubo
  • Víctor Robles
  • Javier Segovia
  • Ernestina Menasalvas
Conference paper
  • 1.4k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3646)

Abstract

All sort of organizations needs as many information about their target population. Public datasets provides one important source of this information. However, the use of these databases is very difficult due to the lack of cross-references.

In Spain, two main public databases are available: Population and Housing Censuses and Family Expenditure Surveys. Both of them are published by Spanish Statistical Institute. These two databases can not be joined due to the different aggregation level (FES contains information about families while PHC contains the same information but aggregated). Besides, national laws protects this information and makes difficult the use of the datasets.

work defines a new methodology for join the two datasets based on Genetic Algorithms. The approach proposed could be used in any case where data with different aggregation level need to be joined.

Keywords

Data Fusion Aggregation Level Economical Index Random Approach Real Index 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Holland, J.H.: Adaption in natural and artificial systems. The University of Michigan Press, Ann Harbor (1975)zbMATHGoogle Scholar
  2. 2.
  3. 3.
    INE (2005), http://www.ine.es
  4. 4.
  5. 5.
  6. 6.
    Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  7. 7.
  8. 8.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs (1982)zbMATHGoogle Scholar
  9. 9.
    Kok, J.N., Van der Putten, P., Gupta, A.: Data fusion through statistical matching. Center for eBussiness@MIT (2002)Google Scholar
  10. 10.
    R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2004) ISBN 3-900051-07-0.Google Scholar
  11. 11.
    Montes, C., Frutos, S., Menasalvas, E., Segovia, J.: Calculating economic indexes per household and censal section from official spanish databases. In: ECML/PKDD (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Óscar Cubo
    • 1
  • Víctor Robles
    • 1
  • Javier Segovia
    • 2
  • Ernestina Menasalvas
    • 2
  1. 1.Departamento de Arquitectura y Tecnología de Sistemas InformáticosUniversidad Politécnica de MadridMadridSpain
  2. 2.Departamento de Lenguajes y SistemasUniversidad Politécnica de MadridMadridSpain

Personalised recommendations