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Proceedings Paper

Genetic algorithms and support vector machines for time series classification
Author(s): Damian R. Eads; Daniel Hill; Sean Davis; Simon J. Perkins; Junshui Ma; Reid B. Porter; James P. Theiler
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Paper Abstract

We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final backend classification. Support vector machines have a reputation for classifying in high-dimensional spaces without overfitting, so the utility of reducing dimensionality with an intermediate feature selection step has been questioned. We address this question by testing Zeus on a lightning classification task using data acquired from the Fast On-orbit Recording of Transient Events (FORTE) satellite.

Paper Details

Date Published: 6 December 2002
PDF: 12 pages
Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); doi: 10.1117/12.453526
Show Author Affiliations
Damian R. Eads, Los Alamos National Lab. and Rochester Institute of Technology (United States)
Daniel Hill, Rochester Institute of Technology (United States)
Sean Davis, Los Alamos National Lab. (United States)
Simon J. Perkins, Los Alamos National Lab. (United States)
Junshui Ma, Los Alamos National Lab. (United States)
Reid B. Porter, Los Alamos National Lab. (United States)
James P. Theiler, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 4787:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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