Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Machine Learning
A new kind of science
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Ensemble learning with local diversity
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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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.