Abstract
In this work, we present a novel algorithm for time series discretization. Our approach includes the optimization of the word size and the alphabet as one parameter. Using evolutionary programming, the search for a good discretization scheme is guided by a cost function which considers three criteria: the entropy regarding the classification, the complexity measured as the number of different strings needed to represent the complete data set, and the compression rate assessed as the length of the discrete representation. Our proposal is compared with some of the most representative algorithms found in the specialized literature, tested in a well-known benchmark of time series data sets. The statistical analysis of the classification accuracy shows that the overall performance of our algorithm is highly competitive.
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García-López, D.-A., Acosta-Mesa, H.-G.: Discretization of Time Series Dataset with a Genetic Search. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds.) MICAI 2009. LNCS, vol. 5845, pp. 201–212. Springer, Heidelberg (2009)
Acosta-Mesa, H.G., Nicandro, C.R., Daniel-Alejandro, G.-L.: Entropy Based Linear Approximation Algorithm for Time Series Discretization. In: Advances in Artificial Intelligence and Applications, vol. 32, pp. 214-224. Research in Computers Science
Dimitrova, E.S., McGee, J., Laubenbacher, E.: Discretization of Time Series Data, (2005) eprint arXiv:q-bio/0505028.
Fayyad, U., Irani, K.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence (1993)
Fogel, L.: Intelligence Through Simulated Evolution. Forty years of Evolutionary Programming (Wiley Series on Intelligent Systems) (1999)
García-López D.A.: Algoritmo de Discretizaciǿn de Series de Tiempo Basado en Entropía y su Aplicaciǿn en Datos Colposcǿpicos. Universidad Veracruzana (2007)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) (2001)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. ACM Trans. Database Syst. (2002)
Keogh, E., Lonardi, S., Ratanamabatana, C.A.: Towards parameter-free data mining. In: Proceedings of Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)
Keogh, E., Xi, C., Wei, L., Ratanamabatana, C.A.: The UCR Time Series Classification/Clustering Homepage (2006), http://www.cs.ucr.edu/~eamonn/time_series_data/
Kurgan, L., Cios, K.: CAIM Discretization Algorithm. IEEE Transactions On Knowledge And Data Engineering (2004)
Last, M., Kandel, A., Bunke, H.: Data mining in time series databases. World Scientific Pub. Co. Inc., Singapore (2004)
Lin, J., Keogh, E., Lonardi, S., Chin, B.: A symbolic representation of time series, with implications for streaming Algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (2003)
Mörchen, F., Ultsch, A.: Optimizing Time Series Discretization for Knowledge Discovery. In: Proceeding of the Eleventh ACM SIGKDD international Conference on Knowledge Discovery in Data Mining (2005)
Kalyanmoy, D., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation (2002)
Trevor, H., Tibshirani, R., Friedman, J.: The elements of Statistical Learning. Springer, Heidelberg (2009)
Chiu, C., Nanh, S.C.: An adapted covering algorithm approach for modeling airplanes landing gravities. Expert Systems with Applications 26, 443–450 (2004)
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Rechy-Ramírez, F., Mesa, HG.A., Mezura-Montes, E., Cruz-Ramírez, N. (2011). Times Series Discretization Using Evolutionary Programming. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_20
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DOI: https://doi.org/10.1007/978-3-642-25330-0_20
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