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2015 | OriginalPaper | Buchkapitel

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

verfasst von : Piotr Artiemjew

Erschienen in: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel The Rough Granular Approach to Classifier Synthesis by Means of SVM

Nächstes Kapitel Extraction of Off-Line Handwritten Characters Based on a Soft K-Segments for Principal Curves

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Titel: The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions
verfasst von: Piotr Artiemjew
Verlag: Springer International Publishing
Buch: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Print ISBN: 978-3-319-25782-2

Electronic ISBN: 978-3-319-25783-9

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-25783-9_24

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