Advertisement

A Bio-inspired Ensemble Model for Food Industry Applications

  • Bruno Baruque
  • Emilio Corchado
  • Jordi Rovira
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)

Abstract

This paper presents a soft computing robust solution for the food industry field with the aim of analysing the olfactory properties of Spanish dry-cured ham. A novel topology preserving version of the Visualization Induced SOM (Vi- SOM), based on the application of the Weighted Voting Superposition (WeVoS) summarization algorithm, is presented in order to calculate the best possible visualization of the internal structure of a datasets. The results obtained by this novel model are compared with the ones obtained by its single version -ViSOM- and versus the well-known SOM and WeVOS-SOM. The results clearly demonstrate how the WeVoS-ViSOM outperforms the rest of models.

Keywords

Soft Computing Electronic Nose Soft Computing Technique Topology Preserve Summarization Algorithm 
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.
    Baruque, B., Corchado, E.: A weighted voting summarization of SOM ensembles. In: Data Mining and Knowledge Discovery, Springer, U.S (2010), doi:10.1007/s10618-009-0160-3Google Scholar
  2. 2.
    Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Information Sciences (Special Issue on Intelligent Distributed Information Systems) 180(10), 2029–2043 (2010), doi:10.1016/j.ins.2009.12.032Google Scholar
  3. 3.
    Kohonen, T.: Self-Organizing Maps, vol. 30. Springer, Berlin (1995)Google Scholar
  4. 4.
    Kohonen, T.: The self-organizing map. Neurocomputing 21(1-3), 1–6 (1998)zbMATHCrossRefGoogle Scholar
  5. 5.
    Kohonen, T., Lehtio, P., Rovamo, J., Hyvarinen, J., Bry, K., Vainio, L.: A principle of neural associative memory. Neuroscience 2(6), 1065–1076 (1977)CrossRefGoogle Scholar
  6. 6.
    Kraaijveld, M.A., Mao, J., Jain, A.K.: A nonlinear projection method based on kohonen’s topology preserving maps. IEEE Transactions on Neural Networks 6(3), 548–559 (1995)CrossRefGoogle Scholar
  7. 7.
    Van der Vorst, J.G.: Performance measurement in agrifood supply chain networks: an overview (2005)Google Scholar
  8. 8.
    Yin, H.: Data visualisation and manifold mapping using the ViSOM. Neural Networks 15(8-9), 1005–1016 (2002)CrossRefGoogle Scholar
  9. 9.
    Yin, H.: ViSOM - a novel method for multivariate data projection and structure visualization. IEEE Transactions on Neural Networks 13(1), 237–243 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bruno Baruque
    • 1
  • Emilio Corchado
    • 2
  • Jordi Rovira
    • 1
  1. 1.University of Burgos 
  2. 2.University of Salamanca 

Personalised recommendations