On the fusion of threshold classifiers for categorization and dimensionality reduction

  • Authors:

  • Hans A. Kestler;Ludwig Lausser;Wolfgang Lindner;Günther Palm

  • Affiliations:

  • University Hospital Ulm, Internal Medicine I, 89081, Ulm, Germany and University of Ulm, Institute of Neural Information Processing, 89069, Ulm, Germany;University Hospital Ulm, Internal Medicine I, 89081, Ulm, Germany;University Hospital Ulm, Internal Medicine I, 89081, Ulm, Germany;University of Ulm, Institute of Neural Information Processing, 89069, Ulm, Germany

  • Venue:
  • Computational Statistics - Special Issue: Proceedings of Reisensburg 2009
  • Year:
  • 2011

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Abstract

We study ensembles of simple threshold classifiers for the categorization of high-dimensional data of low cardinality and give a compression bound on their prediction risk. Two approaches are utilized to produce such classifiers. One is based on univariate feature selection employing the area under the ROC curve as ranking criterion. The other approach uses a greedy selection strategy. The methods are applied to artificial data, published microarray expression profiles, and highly imbalanced data.