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2008

Comparison of Adaboost and ADTboost for Feature Subset Selection

13 years 5 months ago
Comparison of Adaboost and ADTboost for Feature Subset Selection
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.
Martin Drauschke, Wolfgang Förstner
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where PRIS
Authors Martin Drauschke, Wolfgang Förstner
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