C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
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
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Learning cellular automata rules for binary classification problem
The Journal of Supercomputing
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In real world there are many examples where synergetic cooperation of multiple entities performs better than just single one. The same fundamental idea can be found in ensemble learning methods that have the ability to improve classification accuracy. Each classifier has specific view on the problem domain and can produce different classification for the same observed sample. Therefore many methods for combining classifiers into ensembles have been already developed. Most of them use simple majority voting or weighted voting of classifiers to combine the single classifier votes. In this paper we present a new approach for combining classifiers into an ensemble with Classificational Cellular Automata (CCA), which exploit the cellular automata self-organizational abilities. We empirically show that CCA improves the classification accuracy of three popular ensemble methods: Bagging, Boosting and MultiBoosting. The presented results also show important advantages of CCA, such as: problem independency, robustness to noise and no need for the user input.