Optimización de los hiperparámetros de una máquina de regresión de soporte vectorial utilizando enjambre de partículas para el pronóstico de casos de COVID-19

Resumen

En este trabajo se propone un método para la optimización de los hiperparámetros de una máquina de regresión de soporte vectorial mediante la adaptación de la metaheurística de enjambre de partículas. El método se utiliza para pronosticar la serie de tiempo del total de casos positivos acumulados de la reciente enfermedad COVID-19 en la ciudad de Bogotá, Colombia. Para validar el rendimiento del método se establece una comparación con la máquina de regresión de soporte vectorial sin hiperparámetros optimizados, en términos de métricas de medición del rendimiento como lo son el error cuadrático medio, error absoluto medio y el coeficiente de determinación. Con un valor en el error cuadrático medio de 0,000045, un coeficiente de determinación de 0,998884 y el valor-p de 0,0015, para la prueba no paramétrica de Wilcoxon, el método propuesto presenta un mejor desempeño en el pronóstico. Finalmente se pone a discusión la aplicabilidad de este tipo de métodos en el pronóstico de casos en las epidemias.

Palabras clave: covid-19, enjambre de partículas, hiperparámetro, inteligencia de enjambres, máquina de soporte vectorial, metaheurística, optimización, pronóstico, rendimiento, serie de tiempo

Descargas

La descarga de datos todavía no está disponible.

Referencias

[1] J. Zheng, “SARS-coV-2: An emerging coronavirus that causes a global threat,” Int. J. Biol. Sci., vol. 16, no. 10, pp. 1678–1685, 2020, doi: 10.7150/ijbs.45053

[2] A. B. A. Al-Hussein, R. Tahir, “Epidemiological Characteristics of COVID-19 Ongoing Epidemic in Iraq,” Bull. World Heal. Organ., Apr. 2020, doi: 10.2471/BLT.20.251561

[3] E. Estrada, “COVID-19 and SARS-CoV-2. Modeling the present, looking at the future,” Phys. Rep., vol. 869, pp. 1–51, Jul. 2020, doi: 10.1016/j.physrep.2020.07.005

[4] S. Ardabili et al., “COVID-19 Outbreak Prediction with Machine Learning,” SSRN Electron. J., Apr. 2020, doi: 10.1101/2020.04.17.20070094

[5] A. Remuzzi, G. Remuzzi, “COVID-19 and Italy: what next?,” Lancet, vol. 395, no. 10231, pp. 1225–1228, Apr. 2020, doi: 10.1016/S0140-6736(20)30627-9

[6] D. Ivanov, “Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case,” Transp. Res. Part E Logist. Transp. Rev., vol. 136, p. 101922, Apr. 2020, doi: 10.1016/j.tre.2020.101922

[7] F. Koike, N. Morimoto, “Supervised forecasting of the range expansion of novel non-indigenous organisms: Alien pest organisms and the 2009 H1N1 flu pandemic,” Glob. Ecol. Biogeogr., vol. 27, no. 8, pp. 991–1000, Aug. 2018, doi: 10.1111/geb.12754

[8] N. Agarwal, S. Reddy Koti, S. Saran, A. S. Kumar, “Data mining techniques for predicting dengue outbreak in geospatial domain using weather parameters for New Delhi, India,” Curr. Sci., vol. 114, no. 11, 2018, Accessed: Aug. 20, 2020. [Online]. Available: 10.18520/cs/v114/i11/2281-2291

[9] S. S. Chenar, Z. Deng, “Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast,” Environ. Int., vol. 111, pp. 212–223, Feb. 2018, doi: 10.1016/j.envint.2017.11.032

[10] L. Tapak, O. Hamidi, M. Fathian, M. Karami, “Comparative evaluation of time series models for predicting influenza outbreaks: Application of influenza-like illness data from sentinel sites of healthcare centers in Iran,” BMC Res. Notes, vol. 12, no. 1, pp. 1–6, Jun. 2019, doi: 10.1186/s13104-019-4393-y

[11] R. Liang et al., “Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data,” Transbound. Emerg. Dis., vol. 67, no. 2, pp. 935–946, Mar. 2020, doi: 10.1111/tbed.13424

[12] E.-G. Talbi, “Machine Learning for Metaheuristics - State of the Art and Perspectives,” in International Conference on Knowledge and Smart Technology (KST), Apr. 2019, pp. XXIII–XXIII, doi: 10.1109/kst.2019.8687812

[13] L. Calvet, J. De Armas, D. Masip, A. A. Juan, “Learnheuristics: Hybridizing metaheuristics with machine learning for optimization with dynamic inputs,” Open Math., vol. 15, no. 1, pp. 261–280, Jan. 2017, doi: 10.1515/math-2017-0029

[14] M. A. Muñoz, J. A. López, E. F. Caicedo, “Inteligencia de enjambres: sociedades para la solución de problemas (una revisión) Swarm intelligence: problem-solving societies (a review),” Rev. Ing. E Investig., vol. 28, no. 2, pp. 119–130, 2008.

[15] E.-G. Talbi, “Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics,” 2020. Accessed: Aug. 20, 2020. [Online]. Available: https://hal.inria.fr/hal-02745295/document.

[16] J. Kennedy, R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948, doi: 10.1109/ICNN.1995.488968

[17] M. Márquez Gómez, “Las metaheurísticas: tendencias actuales y su aplicabilidad en la ergonomía,” Ing. Ind. Actual. y Nuevas Tendencias, vol. 4, no. 12, pp. 108–120, Jan. 2014.

[18] K. Smets, B. Verdonk, E. M. Jordaan, “Evaluation of performance measures for SVR hyperparameter selection,” in IEEE International Conference on Neural Networks - Conference Proceedings, 2007, pp. 637–642, doi: 10.1109/IJCNN.2007.4371031

[19] W. Naudé, “Artificial intelligence vs COVID-19: limitations, constraints and pitfalls,” AI Soc., vol. 1, p. 3, Apr. 2020, doi: 10.1007/s00146-020-00978-0

[20] Y. Mohamadou, A. Halidou, P. T. Kapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19,” Appl. Intell., pp. 1–13, Jul. 2020, doi: 10.1007/s10489-020-01770-9

[21] R. Sujath, J. M. Chatterjee, A. E. Hassanien, “A machine learning forecasting model for COVID-19 pandemic in India,” Stoch. Environ. Res. Risk Assess., vol. 34, no. 7, pp. 959–972, Jul. 2020, doi: 10.1007/s00477-020-01827-8

[22] L. Xu, “Modelling to contain pandemic influenza A (H1N1) with stochastic membrane systems: A work-in-progress paper,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, 2012, vol. 87 LNICST, pp. 74–81, doi: 10.1007/978-3-642-32615-8_10

[23] M. A. A. Al-Qaness, A. A. Ewees, H. Fan, M. A. El Aziz, “Optimization method for forecasting confirmed cases of COVID-19 in China,” Appl. Sci., vol. 9, no. 3, p. 674, Mar. 2020, doi: 10.3390/JCM9030674

[24] W. Hu, L. Yan, K. Liu, H. Wang, “A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR,” Neural Process. Lett., vol. 43, no. 1, pp. 155–172, Feb. 2016, doi: 10.1007/s11063-015-9409-6

[25] M. Hasanipanah, A. Shahnazar, H. Bakhshandeh Amnieh, D. Jahed Armaghani, “Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model,” Eng. Comput., vol. 33, no. 1, pp. 23–31, Jan. 2017, doi: 10.1007/s00366-016-0453-2

[26] Secretaría Distrital de Salud de Bogotá, “Número de casos confirmados por el laboratorio de COVID- 19 - Bogotá D.C. Datos Abiertos Bogotá,” Apr. 07, 2020. https://datosabiertos.bogota.gov.co/dataset/44eacdb7-a535-45ed-be03-16dbbea6f6da

[27] J. D. Kelleher, B. Mac Namee, A. D’Arcy, Fundamentals of Machine Learning for Predictive Data Analytics. The MIT Press, 2015.

[28] “Normalizar datos: referencia para los módulos - Azure Machine Learning | Microsoft Docs,” Feb. 22, 2020. https://docs.microsoft.com/es-es/azure/machine-learning/algorithm-module-reference/normalize-data

[29] R. Adhikari, R. K. Agrawal, “An Introductory Study on Time Series Modeling and Forecasting,” L. Lambert Acad. Publ., Feb. 2013, Accessed: Aug. 20, 2020. [Online]. Available: http://arxiv.org/abs/1302.6613.

[30] Microsoft, “Ajuste de los hiperparámetros de un modelo - Azure Machine Learning | Microsoft Docs,” Documentación Microsoft, 2020. https://docs.microsoft.com/es-es/azure/machine-learning/how-to-tune-hyperparameters

[31] R. Laref, E. Losson, A. Sava, M. Siadat, “On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications,” Chemom. Intell. Lab. Syst., vol. 184, pp. 22–27, Jan. 2019, doi: 10.1016/j.chemolab.2018.11.011

[32] M. Awad, R. Khanna, M. Awad, R. Khanna, “Support Vector Regression,” in Efficient Learning Machines, Apress, 2015, pp. 67–80.

[33] G. Barrero, “Optimización de hiperparámetros de algoritmos de aprendizaj automático usados para el análisis de la calidad del software,” Res. Gate, no. January, 2019, doi: 10.13140/RG.2.2.15055.74405

[34] S. Kefi, N. Rokbani, A. M. Alimi, “Impact of ant size on ant supervised by PSO, AS-PSO, performances,” in Advances in Intelligent Systems and Computing, Nov. 2017, vol. 552, pp. 567–577, doi: 10.1007/978-3-319-52941-7_56

[35] K. S. Raghuwanshi, “A Qualitative Review of Two Evolutionary Algorithms Inspired by Heuristic Population Based Search Methods: GA & PSO,” in Lecture Notes in Networks and Systems, vol. 18, Singapore: Springer, 2018, pp. 169–175.

[36] K. Demertzis, D. Tsiotas, L. Magafas, “Modeling and forecasting the covid-19 temporal spread in Greece: An exploratory approach based on complex network defined splines,” Int. J. Environ. Res. Public Health, vol. 17, no. 13, pp. 1–18, Jul. 2020, doi: 10.3390/ijerph17134693

[37] S. Sun,Y. Zheng, “Prediction of 2019-nCov in Italy based on PSO and inversion analysis,” medRxiv, May 2020, doi: 10.1101/2020.05.08.20095869

[38] A. Godio, F. Pace, A. Vergnano, “Seir modeling of the italian epidemic of sars-cov-2 using computational swarm intelligence,” Int. J. Environ. Res. Public Health, vol. 17, no. 10, May 2020, doi: 10.3390/ijerph17103535

[39] M. Paggi, “Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?,” arXiv.org, Apr. 2020, Accessed: Aug. 20, 2020. [Online]. Available: http://arxiv.org/abs/2004.08207
Publicado
2021-02-27