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An unsupervised classifier system using soft graph colouring

Author

Listed:
  • Jorge Flores-Cruz
  • Pedro Lara-Velázquez
  • Miguel A. Gutiérrez-Andrade
  • Sergio Gerardo De-los-Cobos-Silva
  • Eric Alfredo Rincón-García
  • Roman Anselmo Mora-Gutiérrez
  • Antonin Ponsich

Abstract

Unsupervised classifiers do not require previous training to achieve their task, on the contrary, they are able to propose alternative classifications that make more sense using raw data instead of human interpretation. In this article an unsupervised classifier system using the soft graph colouring model is presented. This model has the ability to deal with risk and the probability of committing Type I or II errors, for instance, in medical diagnosis, where we want to minimise the risk of a mistaken prognosis. The proposed model is evaluated using some classical instances, and the results are compared with other classifiers, given in all cases solutions as good or better than supervised classifiers.

Suggested Citation

  • Jorge Flores-Cruz & Pedro Lara-Velázquez & Miguel A. Gutiérrez-Andrade & Sergio Gerardo De-los-Cobos-Silva & Eric Alfredo Rincón-García & Roman Anselmo Mora-Gutiérrez & Antonin Ponsich, 2018. "An unsupervised classifier system using soft graph colouring," International Journal of Business Continuity and Risk Management, Inderscience Enterprises Ltd, vol. 8(3), pages 186-199.
    • Handle: RePEc:ids:ijbcrm:v:8:y:2018:i:3:p:186-199
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