Genetic algorithms and support vector machines for time series classification

Damian R. Eads; Daniel Hill; Sean Davis; Simon J. Perkins; Junshui Ma; Reid B. Porter; James P. Theiler

doi:10.1117/12.453526

6 December 2002 Genetic Algorithms and Support Vector Machines for Time Series Classification

Damian R. Eads, Daniel Hill, Sean Davis, Simon J. Perkins, Junshui Ma, Reid B. Porter, James P. Theiler

Proceedings Volume 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V; (2002) https://doi.org/10.1117/12.453526
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

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.

Citation Download Citation

Damian R. Eads, Daniel Hill, Sean Davis, Simon J. Perkins, Junshui Ma, Reid B. Porter, and James P. Theiler "Genetic Algorithms and Support Vector Machines for Time Series Classification", Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); https://doi.org/10.1117/12.453526

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