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Proceedings Paper

Feature selection of signal-averaged electrocardiograms by orthogonal least squares method
Author(s): Michal Raczyk; Stanislaw Jankowski; Ewa Piatkowska-Janko
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Paper Abstract

A crucial problem in machine learning is finding the representative set of data for building a model for both classification and approximation task. In this paper we present the orthogonal least squares method for feature selection. The presented method was used for finding the most important features for selecting patients with sustained ventricular tachycardia after myocardial infarction (SVT+). We show that with the reduced set of descriptors used in the classification process we obtain the results that are better than those obtained with the full set.

Paper Details

Date Published: 6 November 2008
PDF: 7 pages
Proc. SPIE 7124, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008, 71240P (6 November 2008); doi: 10.1117/12.817954
Show Author Affiliations
Michal Raczyk, Warsaw Univ. of Technology (Poland)
Stanislaw Jankowski, Warsaw Univ. of Technology (Poland)
Ewa Piatkowska-Janko, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 7124:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008
Ryszard S. Romaniuk; Tomasz R. Wolinski, Editor(s)

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