Machine-Learning with Cellular Automata

  • Petra Povalej
  • Peter Kokol
  • Tatjana Welzer Družovec
  • Bruno Stiglic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3646)


As the possibility of combining different classifiers into Multiple Classifier System (MCS) becomes an important direction in machine-learning, difficulties arise in choosing the appropriate classifiers to combine and choosing the way for combining their decisions. Therefore in this paper we present a novel approach – Classificational Cellular Automata (CCA). The basic idea of CCA is to combine different classifiers induced on the basis of various machine-learning methods into MCS in a non-predefined way. After several iterations of applying adequate transaction rules only a set of the most appropriate classifiers for solving a specific problem is preserved.

We empirically showed that the superior results compared to AdaBoost ID3 are a direct consequence of self-organization abilities of CCA. The presented results also pointed out important advantages of CCA, such as: problem independency, robustness to noise and no need for user input.


Cellular Automaton Cellular Automaton Learning Sample Purity Measure Decision Tree Induction 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Petra Povalej
    • 1
  • Peter Kokol
    • 1
  • Tatjana Welzer Družovec
    • 1
  • Bruno Stiglic
    • 1
  1. 1.Faculty of Electrical Engineering and Computer ScienceMariborSlovenia

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