Soft Computing in Context-Sensitive Multidimensional Ranking

  • Weber Martins
  • Lauro Eugênio Guimarães Nalini
  • Marco Antonio Assfalk de Oliveira
  • Leonardo Guerra de Rezende Guedes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Many applications require ordering of instances represented by high dimensional vectors. Despite the reasonable quantity of papers on classification and clustering, papers on multidimensional ranking are rare. This paper expands a generic ranking procedure based on one-dimensional self-organizing maps (SOMs). The typical similarity metric is modified to a weighted Euclidean metric and automatically adjusted by a genetic search. The search goal is the best ranking that matches the desired probability distribution (provided by experts) leading to a context-sensitive metric. To ease expert agreement the technique relies on consensus about the best and worst instances. Besides the ranking task, the derived metric is also useful on reducing the number of dimensions (questionnaire items in some situations) and on modeling the data source. Promising results were achieved on the ranking of data from blood bank inspections and client segmentation in agribusiness.


Genetic Algorithm Soft Computing Blood Bank Multidimensional Data Ranking Distribution 
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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  1. 1.
    Martins, W., Meira e Silva, J.C.: Multidimensional Data Ranking Using Self- Organising Maps and Genetic Algorithms. In: Proceedings of IEEE INNS International Joint Conference on Neural Networks (IJCNN 2001), Washington, DC, USA, vol. 4, pp. 2382–2387 (2001)Google Scholar
  2. 2.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: bringing order to the web. Technical report, Stanford University, USA (1998)Google Scholar
  3. 3.
    Kamishima, T., Akaho, S.: Learning From Order Examples. In: Proceedings of The IEEE Intl Conf. on Data Mining, pp. 645–648 (2002)Google Scholar
  4. 4.
    Richardson, M., Domingos, P.: The Intelligent Surfer: Probabilistic combination of link and content information in pagerank. In: Proc. of NIPS 2001 Advances in Neural Information Processing Systems, vol. 14, pp. 1441–1448. MIT Press, Cambridge (2002)Google Scholar
  5. 5.
    Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43, 59–69 (1982)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, 3rd extended edn., vol. 30, Springer, Heidelberg (2001)Google Scholar
  7. 7.
    Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to Order Things. Advances in Neural Information Processing Systems. In: Proc. of NIPS 1997, vol. 11, pp. 451–457. MIT Press, Cambridge (1998)Google Scholar
  8. 8.
    Azcarraga, A.P.: Assessing Self-Organization Using Order Metrics. In: Proceedings of IEEE INNS International Joint Conference on Neural Networks (IJCNN 2000), vol. 6, Piscataway, NJ, USA, pp. 159–164 (2000)Google Scholar
  9. 9.
    Erwin, E., Obermayer, K., Schulten, K.: Self-Organizing Maps: ordering, convergence properties, and energy funcions. Biological Cybernetics 67, 47–55 (1992)MATHCrossRefGoogle Scholar
  10. 10.
    Budinich, M., Taylor, J.G.: On the Ordering Conditions for Self-Organizing Maps. Centre for Neural Networks - Kings College, London, UK (1995)Google Scholar
  11. 11.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)MATHGoogle Scholar
  12. 12.
    Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)Google Scholar
  13. 13.
    Carreira-Perpinan, M.A.: A Review of Dimension Reduction Techniques. Technical Report CS-96-09. University of Shefield, UK (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Weber Martins
    • 1
    • 2
  • Lauro Eugênio Guimarães Nalini
    • 2
  • Marco Antonio Assfalk de Oliveira
    • 1
  • Leonardo Guerra de Rezende Guedes
    • 1
  1. 1.PIRENEUS Research Group, Bloco D, GoianiaFederal University of Goias, School of Computer and Electrical EngineeringGoiasBrazil
  2. 2.Department of Psychology, LAEC Research Group, Bloco H, GoianiaCatholic University of GoiasGoiasBrazil

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