Multiple Boosting in the Ant Colony Decision Forest meta-classifier

Abstract

The idea of ensemble methodology is to combine multiple predictive models in order to achieve a better prediction performance. In this task we analyze the self-adaptive methods for improving the performance of Ant Colony Decision Tree and Forest algorithms. Our goal is to present and compare new meta-ensemble approaches based on Ant Colony Optimization. The proposed meta-classifiers (consisting of homogeneous classifiers) can be characterized by the self-adaptability or the good accommodation with the analyzed data sets and offer appropriate classification accuracy.In this article we provide an overview of ensemble methods in classification tasks and concentrate on the different methodologies, such as Bagging, Boosting and Random Forest. We present all important types of ensemble methods including Boosting and Bagging in context of distributed approach, where agent-ants create better solutions employing adaptive mechanisms. Self adaptive, combining methods and modeling appropriate issues, such as ensembles presented here are discussed in context of the quality of the results. Smaller trees in decision forest without loss of accuracy are achieved during the analysis of different data sets.