Event detection in time series by genetic programming

2012 IEEE Congress on Evolutionary Computation, CEC 2012

Volumn , Issue , 2012, Pages

  Xie, Feng ^a   Song, Andy ^a   Ciesielski, Vic ^a  

^a RMIT UNIVERSITY   (Australia)

Author keywords

event detection; feature extraction; genetic programming; time series

Indexed keywords

CONVENTIONAL METHODS; DOMAIN KNOWLEDGE; EVENT DETECTION; POWER SUPPLY; REAL-WORLD APPLICATION; SYNTHETIC DATASETS; TIME LINE; TIME-SERIES DATA;

FEATURE EXTRACTION; GENETIC PROGRAMMING;

TIME SERIES;

EID: 84866869848     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2012.6256589     Document Type: Conference Paper

References (23)

1. R. Poli and W. Langdon and N. McPhee, A Field Guide to Genetic Programming, Lulu.com,2008
2. J. R. Koza, Genetic programming I:On the programming of computers by means of natural selection, MIT press,1996
3. J. Lohn, G. Hornby, D. Linden,An Evolved Antenna for Deployment on Nasa's Space Technology 5 Mission, Genetic Programming Theory and Practice II, pp. 301-315, Springer(2004)
4. M. A. Kaboudan, Spatiotemporal Forecasting of Housing Price By Use of Genetic Programming, the 16th Annual Meeting of the Association of Global Business, 2004
5. A. Song and B. Pinto, Study of GP representations for motion detection with unstable background, IEEE Congress on Evolutionary Computation, 2010
6. M. Hetland and P. Strom, Temporal Rule Discovery using Genetic Programming and Specialised Hardware, Proceedings of the 4th International Conference on Recent Advances in Soft Computing, 2002
7. N. Wagner and Z. Michalewicz, An analysis of adaptive windowing for time series forecasting in dynamic environments: further tests of the DyFor GP model, pp.1657-1664, Proceedings of the 10th annual conference on Genetic and evolutionary computation, 2008
8. A. Song and F. Xie,Evolving automatic frame splitters,Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011
9. R. C. Holte, Very simple classification rules per- form well on most commonly used datasets, vol. 11, pp.6391, Machine Learning, 1993
10. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993
11. George H. J, Pat L., Estimating Continuous Distributions in Bayesian Classifiers, pp.338345, Eleventh Conference on Uncertainty in Artificial Intelligence, 1995
12. D. Aha, D. Kibler, Instance-based learning algorithms, vol. 6, pp.3766, Machine Learning, 1991
13. Y. Freund, E. S. Robert, Experiments with a new boosting algorithm, pp.148156, Thirteenth International Conference on Machine Learning, 1996
14. V. Guralnik and J. Srivastava, Event detection from time series data, pp.33-42, Proceedings of the fifth ACM SIGKDD international conference on Knowledge,1999
15. J. Hunter and N. McIntosh, Knowledge-based event detection in complex time series data, pp.271-280, Artificial Intelligence in Medicine,1999
16. D. Preston and P. Protopapas and C. Brodley, Event discovery in time series, pp.61-72, Proceedings of the SIAM International Conference on Data Mining, 2009
17. E. Keogh and J. Lin and A. Fu, Hot sax: Efficiently finding the most unusual time series subsequence, Fifth IEEE International Conference on Data Mining, 2005
18. C. A. Ratanamahatana and J. Lin and D. Gunopulos and E. Keogh and M. Vlachos and G. Das, Mining time series data, Data Mining and Knowledge Discovery Handbook 2010, pp. 1049-1077, 2010
19. C. Shahabi and D. Yan, Real-time pattern isolation and recognition over immersive sensor data streams, pp. 93-113, Proceedings of The 9th Intl Conference on Multi-Media Modeling, 2003
20. F. Yoav, E. S.Robert, Experiments with a new boosting algorithm, pp.148-156,Thirteenth International Conference on Machine Learning, 1996
21. L.Wei and E. Keogh, Semi-supervised time series classification, pp. 748-753, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006
22. A. Song, V. Ciesielski, Texture Segmentation by Genetic Programming, Evolutionary Computation, pp. 461-481, 2008
23. T. Loveard, V. Ciesielski, Representing classification problems in genetic programming, Proceedings of the 2001 Congress on Evolutionary Computation, pp. 1070-1077, 2001