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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 3/2005

01.12.2005 | Theoretical Advances

A dempster-shafer theoretic framework for boosting based ensemble design

verfasst von: Hakan Altınçay

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2005

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Abstract

Training set resampling based ensemble design techniques are successfully used to reduce the classification errors of the base classifiers. Boosting is one of the techniques used for this purpose where each training set is obtained by drawing samples with replacement from the available training set according to a weighted distribution which is modified for each new classifier to be included in the ensemble. The weighted resampling results in a classifier set, each being accurate in different parts of the input space mainly specified the sample weights. In this study, a dynamic integration of boosting based ensembles is proposed so as to take into account the heterogeneity of the input sets. An evidence-theoretic framework is developed for this purpose so as to take into account the weights and distances of the neighboring training samples in both training and testing boosting based ensembles. The effectiveness of the proposed technique is compared to the AdaBoost algorithm using three different base classifiers.
Vorheriger Artikel A hybrid post-processing system for offline handwritten Chinese script recognition
Nächster Artikel Estimation of generalized entropies with sample spacing

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Metadaten
Titel
A dempster-shafer theoretic framework for boosting based ensemble design
verfasst von
Hakan Altınçay
Publikationsdatum
01.12.2005
Verlag
Springer-Verlag
Erschienen in
Pattern Analysis and Applications / Ausgabe 3/2005
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-005-0010-x

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