Comparison of Adaboost and ADTboost for Feature Subset Selection

Martin Drauschke, Wolfgang Förstner

Abstract

This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting methods, Adaboost and ADTboost. In our evaluation, we have focused on three different criteria: the classification error and the efficiency of the process depending on the number of most appropriate features and the number of training samples. Therefore, we discuss both techniques and sketch their functionality, where we restrict both boosting approaches to linear weak classifiers. We propose a feature subset selection method, which we evaluate on synthetic and on benchmark data sets.

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Paper Citation


in Harvard Style

Drauschke M. and Förstner W. (2008). Comparison of Adaboost and ADTboost for Feature Subset Selection . In Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2008) ISBN 978-989-8111-42-5, pages 113-122. DOI: 10.5220/0001741201130122


in Bibtex Style

@conference{pris08,
author={Martin Drauschke and Wolfgang Förstner},
title={Comparison of Adaboost and ADTboost for Feature Subset Selection},
booktitle={Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2008)},
year={2008},
pages={113-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001741201130122},
isbn={978-989-8111-42-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2008)
TI - Comparison of Adaboost and ADTboost for Feature Subset Selection
SN - 978-989-8111-42-5
AU - Drauschke M.
AU - Förstner W.
PY - 2008
SP - 113
EP - 122
DO - 10.5220/0001741201130122