Abstract
A state in time series can be referred as a certain signal pattern occurring consistently for a long period of time. Learning such a pattern can be useful in automatic identification of the time series state for tasks like activity recognition. In this study we showcase the capability of our GP-based time series analysis method on learning different types of states from multi-channel stream input. This evolutionary learning method can handle relatively complex scenarios using only raw inputs requiring no features. The method performed very well on both artificial time series and real world human activity data. It can be competitive comparing with classical learning methods on features.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Bernad, D.J.: Finding patterns in time series: a dynamic programming approach. In: Advances in Knowledge Discovery and Data Mining (1996)
Brooks, R.R., Ramanathan, P., Sayeed, A.M.: Distributed target classification and tracking in sensor networks. Proceedings of the IEEE 91(8), 1163–1171 (2003)
Chan, K.-P., Fu, A.W.-C.: Efficient time series matching by wavelets. In: Proceedings of the 15th International Conference on Data Engineering, pp. 126–133. IEEE (1999)
Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M.: Classification of the myoelectric signal using time-frequency based representations. Medical Engineering & Physics 21(6), 431–438 (1999)
Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of linear, nonlinear, and feature selection methods for eeg signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2), 141–144 (2003)
Nanopoulos, A., Alcock, R., Manolopoulos, Y.: Feature-based classification of time-series data. International Journal of Computer Research 10(3) (2001)
Ralanamahatana, C., Lin, J., Gunopulos, D., Keogh, E., Vlachos, M., Das, G.: Mining time series data. Data Mining and Knowledge Discovery Handbook, 1069–1103 (2005)
Rasoul Safavian, S.: David Landgrebe. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics 21(3), 660–674 (1991)
Subasi, A.: Eeg signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 32(4), 1084–1093 (2007)
Way, M.J., Scargle, J.D., Ali, K.M., Srivastava, A.N.: Advances in Machine Learning and Data Mining for Astronomy. CRC Press, Boca Raton (2012)
Xie, F., Song, A., Ciesielski, V.: Genetic programming based activity recognition on a smartphone sensory data benchmark. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 2917–2924. IEEE (2014)
Xie, F., Song, A., Ciesielski, V.: Event detection in time series by genetic programming. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)
Xing, Z., Pei, J., Keogh, E.: A brief survey on sequence classification. ACM SIGKDD Explorations Newsletter 12(1), 40–48 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xie, F., Song, A., Ciesielski, V. (2014). Learning Patterns of States in Time Series by Genetic Programming. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-13563-2_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13562-5
Online ISBN: 978-3-319-13563-2
eBook Packages: Computer ScienceComputer Science (R0)