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

  • Piotr ArtiemjewEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)

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.

Keywords

Rough sets Decision systems Bootstrap ensemble   Ada-Boost Arcing 

Notes

Acknowledgements

The research has been supported by grant 1309-802 from Ministry of Science and Higher Education of the Republic of Poland.

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Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of Warmia and MazuryOlsztynPoland

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