Advertisement

An Study of the Tree Generation Algorithms in Equation Based Model Learning with Low Quality Data

  • Alba Berzosa
  • José R. Villar
  • Javier Sedano
  • Marco García-Tamargo
  • Enrique de la Cal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6679)

Abstract

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. In previous works the learning models using the so-called Low Quality Data (LQD) has been studied in order to analyze the way to represent the uncertainty. It makes use of genetic programming and the multiobjective simmulated annealing heuristic, which has been hybridized with genetic operators. The role of the tree generation methods when learning LQD was studied in that paper. The present work deals with the analysis of the generation methods relevance in depth and provides with statistical studies on the obtained results.

Keywords

Genetic Programming Genetic Algorithm and Programming Low Quality Data Multiobjective Simulated Annealing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alcalá-fdez, J., Sánchez, L., García, S., Del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Bacardit, J., Rivas, V.M., Fernández, J.C., Herrera, F.: Keel: A software tool to assess evolutionary algorithms for data mining problems?Google Scholar
  2. 2.
    Berzosa, B., Villar, J.R., Sedano, M., García-Tamargo, J.: Tree generation methods comparison in gap problems with low quality data. In: Accepted for publication in the Proceedings of the International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2011. AISC (2011)Google Scholar
  3. 3.
    Corchado, E., Herrero, A.: Neural visualization of network traffic data for intrusion detection. Applied Soft Computing (2010)Google Scholar
  4. 4.
    Ferson, S., Kreinovich, V., Hajagos, J., Oberkampf, W., Ginzburg, L.: Experimental uncertainty estimation and statistics for data having interval uncertainty. Technical report, Technical Report SAND2007-0939 (2007), http://www.ramas.com/intstats.pdf
  5. 5.
    Folleco, A., Khoshgoftaar, T.M., Hulse, J.V., Napolitano, A.: Identifying learners robust to low quality data. Informatica (Slovenia) 33(3), 245–259 (2009)MathSciNetzbMATHGoogle Scholar
  6. 6.
    De Keyser, R., Ionescu, C.: Modelling and simulation of a lighting control system. Simulation Modelling Practice and Theory 18(2), 165–176 (2010)CrossRefGoogle Scholar
  7. 7.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  8. 8.
    Li, D.H.W., Cheung, K.L., Wong, S.L., Lam, T.N.T.: An analysis of energy-efficient light fittings and lighting controls. Applied Energy 87(2), 558–567 (2010)CrossRefGoogle Scholar
  9. 9.
    Luke, S.: Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation 4(3), 274–283 (2000)CrossRefGoogle Scholar
  10. 10.
    Sánchez, L., Rosario Suárez, M., Villar, J.R., Couso, I.: Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data. Int. J. Approx. Reasoning 49, 607–622 (2008)CrossRefGoogle Scholar
  11. 11.
    Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A Soft Computing Method for Detecting Lifetime Building Thermal Insulation Failures. Integr. Comput. Aided Eng. 17, 103–115 (2010)Google Scholar
  12. 12.
    Soule, T., Foster, J.A., Dickinson, J.: Code growth in genetic programming. In: Proceedings of the First Annual Conference on Genetic Programming GECCO 1996, pp. 215–223. MIT Press, Cambridge (1996)Google Scholar
  13. 13.
    Villar, J.R., de la Cal, E., Sedano, J., García-Tamargo, M.: Analysing the low quality of the data in lighting control systems. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS, vol. 6076, pp. 421–428. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Villar, J., Otero, A., Otero, J., Sánchez, L.: Taximeter verification with gps and soft computing techniques. Soft Comput. 14, 405–418 (2009)CrossRefGoogle Scholar
  15. 15.
    Villar, J.R., Berzosa, A., de la Cal, E., Sedano, J., García-Tamargo, M.: Multi-objecve simulated annealing in genetic algorithm and programming learning with low quality data. Submitted to Neural Computing (2010)Google Scholar
  16. 16.
    Villar, J.R., de la Cal, E., Sedano, J.: A fuzzy logic based efficient energy saving approach for domestic heating systems. Integrated Computer-Aided Engineering 16, 151–163 (2009)Google Scholar
  17. 17.
    Yu, W.-D., Sedano, L.Y.-C.: Ahybridization of cbr and numeric soft computing techniques for mining of scarce construction databases. Autom. in Constr. 15, 33–46 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

Personalised recommendations