Improving Ensembles with Classificational Cellular Automata

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 selforganizational 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.