The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions

Abstract

In the recent works we have investigated the classifiers based on weak rough inclusions, especially the 8v1.1 - 8v1.5 algorithms. These algorithms in process of weights forming for classification dynamically react on the distance between the particular attributes. Our results show the effectiveness of these methods and the wide application in many contexts, especially in the context of classification of DNA Microarray data. In this work we have checked a few methods for classifier stabilisation, such as the Bootstrap Ensemble, Boosting based on Arcing, and Ada-Boost with Monte Carlo split. We have performed experiments on selected data from the UCI Repository. The results show that the committee of weak classifiers stabilised our algorithms in the context of accuracy of classification. The Boosting based on Arcing turned out to be the most promising method among those examined.