Comparison of Fuzzy Functions for Low Quality Data GAP Algorithms

  • Enrique de la Cal
  • José R. Villar
  • Marco García-Tamargo
  • Javier Sedano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7209)


The undesired effects of data gathered from real world can be produced by the noise in the process, the bias of the sensors and the presence of hysteresis, among other uncertainty sources.

Data gathered by this way are called Low Quality Data (LQD). Thus, uncertainty representation tools are needed for using in learning models with this kind of data.

This work presents a method to represent the uncertainty and an approach for learning white box Equation Based Models (EBM). The proficiency of the representations with different noise levels and fitness functions typology is compared.

The numerical results show that the use of the described objectives improves the proficiency of the algorithms. It has been also proved that each meta-heuristic determines the typology of fitness function.


Low Quality Data Simulated Annealing Genetic Programming Algorithm Equation Based Model 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Enrique de la Cal
    • 1
  • José R. Villar
    • 1
  • Marco García-Tamargo
    • 1
  • Javier Sedano
    • 2
  1. 1.Computer Science DepartmentUniversity of OviedoGijónSpain
  2. 2.Instituto Tecnológico de Castilla y LeónBurgosSpain

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