Advertisement

Static Field Approach for Pattern Classification

  • Dymitr Ruta
  • Bogdan Gabrys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2311)

Abstract

Recent findings in pattern recognition show that dramatic improvement of the recognition rate can be obtained by application of fusion systems utilizing many different and diverse classifiers for the same task. Apart from a good individual performance of individual classifiers the most important factor is the useful diversity they exhibit. In this work we present an example of a novel non-parametric classifier design, which shows a substantial level of diversity with respect to other commonly used classifiers. In our approach inspiration for the new classification method has been found in the physical world. Namely we considered training data as particles in the input space and exploited the concept of a static field acting upon the samples. Specifically, every single data point used for training was a source of a central field, curving the geometry of the input space. The classification process is presented as a translocation in the input space along the local gradient of the field potential generated by the training data. The label of a training sample to which it converged during the translocation determines the eventual class label of the new data point. Based on selected simple fields found in nature, we show extensive examples and visual interpretations of the presented classification method. The practical applicability of the new model is examined and tested using well-known real and artificial datasets.

Keywords

Training Data Input Space Classification Process Independent Component Analysis Pattern Classification 
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.
    Duda R.O., Hart P.E., Stork D.G.: Pattern Classification. John Wiley & Sons, New York (2001).zbMATHGoogle Scholar
  2. 2.
    Bezdek J.C.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic, Boston (1999).zbMATHGoogle Scholar
  3. 3.
    Sharkey A.J.C.: Combining Artificial Neural Nets: Ensemble and Modular Multi-net Systems. Springer-Verlag, Berlin Heidelberg New York (1999).zbMATHGoogle Scholar
  4. 4.
    Sharkey A.J.C., Sharkey N.E.: Combining Diverse Neural Nets. The Knowledge Engineering Review 12(3) (1997) 231–247.CrossRefGoogle Scholar
  5. 5.
    Kuncheva L.I., Whitaker C.J.: Ten Measures of Diversity in Classifier Ensembles: Limits for Two Classifiers. IEE Workshop on Intelligent Sensor Processing, Birmingham (2001) 10/1–10/6Google Scholar
  6. 6.
    Ruta D., Gabrys B.: Analysis of the Correlation Between Majority Voting Errors and the Diversity Measures in Multiple Classifier Systems. International Symposium on Soft Computing, Paisley (2001).Google Scholar
  7. 7.
    Zurek W.H.: Complexity, Entropy and the Physics of Information. Proc. of the Workshop on Complexity, Entropy, and the Physics of Information. Santa Fe (1989).Google Scholar
  8. 8.
    Klir G.J., Folger T.A.: Fuzzy Sets, Uncertainty, and Information. Prentice-Hall International Edition (1988).Google Scholar
  9. 9.
    Hochreiter S., Mozer M.C.: An Electric Approach to Independent Component Analysis. Proc. of the Second International Workshop on Independent Component Analysis and Signal Separation, Helsinki(2000) 45–50.Google Scholar
  10. 10.
    Principe J., Fisher III, Xu D.: Information Theoretic Learning. In S. Haykin (Ed.): Unsupervised Adaptive Filtering. New York NY (2000).Google Scholar
  11. 11.
    Torkkola K., Campbell W.: Mutual Information in Learning Feature Transformations. Proc. of International Conference on Machine Learning, Stanford CA (2000).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Dymitr Ruta
    • 1
  • Bogdan Gabrys
    • 1
  1. 1.Applied Computational Intelligence Research Unit Division of Computing and Information SystemsUniversity of PaisleyPaisleyUK

Personalised recommendations