3 research outputs found

    Genetic programming and serial processing for time series classification

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    This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for online or conference competitions. As there are published results of these two problems this gives us the chance to compare the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large datasets.Alfaro Cid, E.; Sharman, KC.; Esparcia Alcázar, AI. (2014). Genetic programming and serial processing for time series classification. Evolutionary Computation. 22(2):265-285. doi:10.1162/EVCO_a_00110S265285222Adeodato, P. J. L., Arnaud, A. L., Vasconcelos, G. C., Cunha, R. C. L. V., Gurgel, T. B., & Monteiro, D. S. M. P. (2009). The role of temporal feature extraction and bagging of MLP neural networks for solving the WCCI 2008 Ford Classification Challenge. 2009 International Joint Conference on Neural Networks. doi:10.1109/ijcnn.2009.5178965Alfaro-Cid, E., Merelo, J. J., de Vega, F. F., Esparcia-Alcázar, A. I., & Sharman, K. (2010). Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study. Evolutionary Computation, 18(2), 305-332. doi:10.1162/evco.2010.18.2.18206Alfaro-Cid, E., Sharman, K., & Esparcia-Alcazar, A. I. (s. f.). Evolving a Learning Machine by Genetic Programming. 2006 IEEE International Conference on Evolutionary Computation. doi:10.1109/cec.2006.1688316Arenas, M. G., Collet, P., Eiben, A. E., Jelasity, M., Merelo, J. J., Paechter, B., … Schoenauer, M. (2002). A Framework for Distributed Evolutionary Algorithms. Lecture Notes in Computer Science, 665-675. doi:10.1007/3-540-45712-7_64Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T. M., Schalk, G., Wolpaw, J. R., … Birbaumer, N. (2004). The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials. IEEE Transactions on Biomedical Engineering, 51(6), 1044-1051. doi:10.1109/tbme.2004.826692Borrelli, A., De Falco, I., Della Cioppa, A., Nicodemi, M., & Trautteur, G. (2006). Performance of genetic programming to extract the trend in noisy data series. Physica A: Statistical Mechanics and its Applications, 370(1), 104-108. doi:10.1016/j.physa.2006.04.025Eads, D. R., Hill, D., Davis, S., Perkins, S. J., Ma, J., Porter, R. B., & Theiler, J. P. (2002). Genetic Algorithms and Support Vector Machines for Time Series Classification. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V. doi:10.1117/12.453526Eggermont, J., Eiben, A. E., & van Hemert, J. I. (1999). A Comparison of Genetic Programming Variants for Data Classification. Lecture Notes in Computer Science, 281-290. doi:10.1007/3-540-48412-4_24Holladay, K. L., & Robbins, K. A. (2007). Evolution of Signal Processing Algorithms using Vector Based Genetic Programming. 2007 15th International Conference on Digital Signal Processing. doi:10.1109/icdsp.2007.4288629Kaboudan, M. A. (2000). Computational Economics, 16(3), 207-236. doi:10.1023/a:1008768404046Kishore, J. K., Patnaik, L. M., Mani, V., & Agrawal, V. K. (2000). Application of genetic programming for multicategory pattern classification. IEEE Transactions on Evolutionary Computation, 4(3), 242-258. doi:10.1109/4235.873235Kishore, J. K., Patnaik, L. M., Mani, V., & Agrawal, V. K. (2001). Genetic programming based pattern classification with feature space partitioning. Information Sciences, 131(1-4), 65-86. doi:10.1016/s0020-0255(00)00081-5Langdon, W. B., McKay, R. I., & Spector, L. (2010). Genetic Programming. International Series in Operations Research & Management Science, 185-225. doi:10.1007/978-1-4419-1665-5_7Yi Liu, & Khoshgoftaar, T. (s. f.). Reducing overfitting in genetic programming models for software quality classification. Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings. doi:10.1109/hase.2004.1281730Luke, S. (2000). Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation, 4(3), 274-283. doi:10.1109/4235.873237Luke, S., & Panait, L. (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation, 14(3), 309-344. doi:10.1162/evco.2006.14.3.309Mensh, B. D., Werfel, J., & Seung, H. S. (2004). BCI Competition 2003—Data Set Ia: Combining Gamma-Band Power With Slow Cortical Potentials to Improve Single-Trial Classification of Electroencephalographic Signals. IEEE Transactions on Biomedical Engineering, 51(6), 1052-1056. doi:10.1109/tbme.2004.827081Muni, D. P., Pal, N. R., & Das, J. (2006). Genetic programming for simultaneous feature selection and classifier design. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 36(1), 106-117. doi:10.1109/tsmcb.2005.854499Oltean, M., & Dioşan, L. (2009). An autonomous GP-based system for regression and classification problems. Applied Soft Computing, 9(1), 49-60. doi:10.1016/j.asoc.2008.03.008Otero, F. E. B., Silva, M. M. S., Freitas, A. A., & Nievola, J. C. (2003). Genetic Programming for Attribute Construction in Data Mining. Genetic Programming, 384-393. doi:10.1007/3-540-36599-0_36Poli, R. (2010). Genetic programming theory. Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO ’10. doi:10.1145/1830761.1830905Tsakonas, A. (2006). A comparison of classification accuracy of four genetic programming-evolved intelligent structures. Information Sciences, 176(6), 691-724. doi:10.1016/j.ins.2005.03.012Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., McFarland, D. J., Peckham, P. H., Schalk, G., … Vaughan, T. M. (2000). 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    On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

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    This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p
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