Genetic algorithms and support vector machines for time series classification

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 4787, Issue , 2002, Pages 74-85

  Eads, Damian ^a,b   Hill, Daniel ^b   Davis, Sean ^a   Perkins, Simon ^a   Ma, Junshui ^a   Porter, Reid ^a   Theiler, James ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b Rochester Institute of Technology   (United States)

Author keywords

Feature selection; Genetic algorithm; Genetic programming; Lightning; N fold cross validation; Support vector machines; Time series classification; Tornado

Indexed keywords

GENETIC ALGORITHMS; LEARNING ALGORITHMS; LIGHTNING; MATHEMATICAL PROGRAMMING; TIME SERIES ANALYSIS; TORNADOES;

FAST ON-ORBIT RECORDING OF TRANSIENT EVENTS; FEATURE SELECTION; SUPPORT VECTOR MACHINES; TIME SERIES CLASSIFICATION;

FEATURE EXTRACTION;

EID: 0036993130     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.453526     Document Type: Conference Paper

References (18)

1. S. Briles, K. Moore, R. Jones, P. Blain, P. Klinger, D. Neagley, M. Caffrey, K. Henneke, and W. Spurgen, Innovative Use of DSP Technology in Space: FORTE Event Classifier, Proceedings of the International Workshop on Artificial Intelligence in Solar-Terrestrial Physics, 1993.
2. K. Moore, P.C. Blain, S.D. Briles, and R.G. Jones, Classification of RF transients in space using digital signal processing and neural network techniques, Proc. SPIE 2492, 1997.
3. S.P. Brumby, N.R. Harvey, S. Perkins, R.B. Porter, J.J. Szymanski, J. Theiler, and J.J. Bloch, A genetic algorithm for combining new and existing image processing tools for multispectral imagery, Proc. SPIE 4049, 2000.
4. N.R. Harvey, J. Theiler, S.P. Brumby, S. Perkins, J.J. Szymanski, J.J. Bloch, R.B. Porter, M. Galassi, and A.C. Young, Comparison of GENIE and Conventional Superviced Classifiers for Multispectral Image Feature Extraction, IEEE. Transactions on Geoscience and Remote Sensing, 40 (2002) 393-404.
5. S. Perkins, N.R. Harvey, S.P. Brumby and K. Lacker, Support Vector Machines for Broad Area Feature Extraction in Remotely Sensed Images, Proc. SPIE 4381, 2001.
6. J.P. Szymanski, S.P. Brumby, P. Pope, D.R. Eads, D. Esch-Mosher, M. Galassi, N.R. Harvey, H.D.W. McCulloch, S.J. Perkins, R. Porter, J. Theiler, A.C. Young, J.J. Bloch, and N. David, Feature Extraction from Multiple Data Sources using Genetic Programming, Proc. SPIE 4725, 2002.
7. J. Theiler, N.R. Harvey, S.P. Brumby, J.J. Szymanski, S. Alferink, S. Perkins, R. Porter, and J.J. Bloch, Evolving Retrieval Algorithms with a Genetic Programming Scheme, Proc. SPIE 3753, pp. 416-425, 1999.
8. Duda, Richard and Peter Hart, Pattern Classification and Scene Analysis, John Wiley and Sons, New York, NY, 1973.
9. Nello Cristianini, An Introduction to Support Vector Machines, Cambridge University Press, 2000.
10. O. Barzilay and V.L. Brailovsky, On domain knowledge and feature selection using a support vector machines, Pattern Recognition Letters, Volume 20, Number 5, pp. 475-484, May 1999.
11. C. Burgess, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, Volume 2, Number 2, pp. 121-167, 1998.
12. V.N. Vapnik, The nature of statistical learning theory, Springer-Verlag, New York, 1995.
13. L.J. Cao and F.E. Tay, Feature selection for support vector machines in financial time series forecasting, Intelligent Data Engineering and Automated Learning: Data Mining, Financial Engineering, and Intelligent Agents, Second International Conference Proceedings, Springer-Verlag, December, 2000.
14. R. J. Abrahart, Linda See, and P. E. Kneale, Applying Saliency Analysis to Neural Network Rainfall - Runoff Modelling, GeoComputation 99, http://www.geovista.psu.edu/sites/geocomp99/Gc99/003/gc_003.htm, 1999.
15. E.H. Tay, and L.J. Cao, A comparative study of saliency analysis and genetic algorithm for feature selection in support vector machines, Intelligent Data Analysis, 2001, Volume 5, Number 3, pp. 191-209.
16. W. Banzhaf, Genetic Programming: An introduction on the automatic evolution of computer programs and its applications, Morgan Kaufmann Publishers, San Francisco, 1998.
17. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
18. C.C. Chang and C.J. Lin, LIBSVM: a Library for Support Vector Machines (Version 2.33), http://www.csie.ntu.edu.tw/~cjlin/libsvm/, 2002.