Abstract
The considerable technological evolution during the last deca-des has made it possible to deal with biological datasets of increasing higher dimensionality, such as those used in BCI applications. Thus, techniques such as feature selection, which allow obtaining the underlying information of these datasets by removing those features considered redundant or noisy, have emerged. Over the years, wrapper approaches based on evolutionary algorithms have gained great relevance, as they have proven to be one of the best procedures to tackle this problem, with NSGA-II being one of the most used search strategies. Historically, these procedures have presented a well-known bottleneck in the evaluation method. However, a more significant bottleneck appears when dealing with high-dimensional datasets, which lies in the application of the NSGA-II’s selection method to very large populations. For this reason, this paper aims to alleviate this problem and, consequently, develop a parallel strategy able to reach a superlinear speedup.
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Notes
- 1.
The last value of \(N_{sp}\) is set to 24 since that is the maximum number of CPU physical cores where the wrapper is executed.
References
Asensio-Cubero, J., Gan, J.Q., Palaniappan, R.: Multiresolution analysis over simple graphs for brain computer interfaces. J. Neural Eng. 10(4), 21–26 (2013). http://doi.org/10.1088/1741-2560/10/4/046014
Bellman, R.E.: Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961)
BioSemi: Biosemi System. https://www.biosemi.com/products.htm. Accessed 02 Dec 2019
Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1, 131–156 (1997). https://doi.org/10.1016/S1088-467X(97)00008-5
Karami, E.M.: Explaining the python global interpreter lock. https://www.obytes.com/blog/explaining-the-python-global-interpreter-lock. Accessed 24 Sept 2020
Fortin, F.A., De Rainville, F.M., Gardner, M.A., M. P., Gagn, C.: Tournament selection based on dominance. https://deap.readthedocs.io/en/master/api/tools.html#deap.tools.selTournamentDCD. Accessed 14 Sept 2020
González, J., Ortega, J., Damas, M., Martín-Smith, P., Gan, J.Q.: A new multi-objective wrapper method for feature selection - accuracy and stability analysis for BCI. Neurocomputing, 407–418 (2019). https://doi.org/10.1016/j.neucom.2019.01.017
Muhlenbein, H., Schlierkamp-Voosen, D.: Optimal interaction of mutation and crossover in the breeder genetic algorithm. In: Proceedings of the Fifth International Conference on Genetic Algorithms, p. 648. Citeseer, Morgan Kaufmann Publishers Inc., San Francisco (1993)
Pérez, F., Granger, B.E., Hunter, J.D.: Python: an ecosystem for scientific computing. Comput. Sci. Eng. 13(2), 13–21 (2011). https://doi.org/10.1109/MCSE.2010.119
Tang, J., Alelyani, S., Liu, H.: Feature selection for classification: a review. Data Classif. Algorithms Appl. 37–64 (2014)
Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-29938-6
Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20, 606–626 (2016). http://doi.org/10.1109/TEVC.2015.2504420
Yin, Z., Lan, H., Tan, G., Lu, M., Vasilakos, A.V., Liu, W.: Computing platforms for big biological data analytics: perspectives and challenges. Comput. Struct. Biotechnol. J. 15, 403–411 (2017). https://doi.org/10.1016/j.csbj.2017.07.004
Acknowledgment
This research has been funded by the Spanish Ministry of Science, Innovation, and Universities (grants PGC2018-098813-B-C31 and PID2022-137461NB-C31) and the ERDF fund. We would like to thank the BCI laboratory of the University of Essex, especially Dr. John Q. Gan, for allowing us to use their datasets.
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Gómez-López, J.C., Castillo-Secilla, D., Kimovski, D., González, J. (2023). Boosting NSGA-II-Based Wrappers Speedup for High-Dimensional Data: Application to EEG Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_7
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