Abstract
This paper describes the developed SGD-based Wiener polynomial approximation method for the missing data recovery of air pollution monitoring tasks. The main steps of algorithmic implementation of the method have been described and the necessity of a combination of both of these tools is substantiated. The basic parameters of the method (the degree of the polynomial, the loss function of the SGD algorithm) for design an optimal variant of it are experimentally investigated. One out of four studied loss functions was chosen for the practical implementation of the method for the design of the future applied air pollution monitoring system. It is founded that high degrees of the Wiener polynomial significantly increase the training time with a slight increase in accuracy. That’s why a second-degree polynomial was chosen. The simulation of the method showed high as accuracy (based on MAPE, RMSE, MAE) and low computation time. Comparison of the developed method’s results with the existing regression analysis methods (Adaptive Boosting, GRNN, SVR with different kernels) confirmed the high efficiency of its work. The proposed combination of the method allows obtaining an effective result from the point of view of accuracy-speed for the large volumes of data processing. The developed method will be useful when solving different tasks, for example, for a smart home or a smart city, medicine, economics, etc. That is, for those tasks where the problem of missing data does not allow conducting further effective intellectual analysis.
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Duryahina, Z.A., Kovbasyuk, T.M., Bespalov, S.A., et al.: Micromechanical and electrophysical properties of Al2O3 nanostructured dielectric coatings on plane heating elements. Mater. Sci. 52, 50 (2016)
Atmospheric chemistry. https://www.tankonyvtar.hu/hu/tartalom/tamop412A/2011-0073_atmospheric_chemistry/adatok.html. Accessed 09 Feb 2019
Artem, K., Ivan, T., Vasyl, T.: Intelligent house as a service and its practical usage for home energy efficiency. In 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 220–223 (2017)
Boreiko, O., Teslyuk, V.: Developing a controller for registering passenger flow of public transport for the ‘smart’ city system. East. Eur. J. Enterp. Technol. 6(3(84)), 40–46 (2016)
Vynokurova, O., Peleshko, D., Oskerko, S., Lutsan, V., Peleshko, M.: Multidimensional wavelet neuron for pattern recognition tasks in the internet of things applications. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2018. AISC, vol. 754, pp. 64–73. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91008-6_7
Shakhovska, N., Shamuratov, O.: The structure of information systems for environmental monitoring. In: 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT), pp. 102–107 (2016)
Lytvyn, V., Vysotska, V., Veres, O., et al.: The risk management modelling in multi project environment. In: 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 32–35 (2017)
Riznyk, O., Yurchak, I., Povshuk, O.: Synthesis of optimal recovery systems in distributed computing using ideal ring bundles. In: 2016 XII International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), pp. 220–222 (2016)
Babichev, S., Lytvynenko, V., Gozhyj, A., Korobchynskyi, M., Voronenko, M.: A fuzzy model for gene expression profiles reducing based on the complex use of statistical criteria and shannon entropy. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2018. AISC, vol. 754, pp. 545–554. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91008-6_55
Molner, E., Molner, R., Kryvinska, N., et al.: Web intelligence in practice. J. Serv. Sci. Res. 6, 149 (2014). https://doi.org/10.1007/s12927-014-0006-4
Kaczor, S., Kryvinska, N.: It is all about services - fundamentals, drivers, and business models. J. Serv. Sci. Res. 5(2), 125–154 (2013)
Gregus, M., Kryvinska, N.: Service orientation of enterprises - aspects, dimensions, technologies. Comenius University in Bratislava (2015)
Kryvinska, N., Gregus, M.: SOA and its business value in requirements, features, practices and methodologies. Comenius University in Bratislava (2014)
Gheyas, I.A., Smith, L.S.: A neural network-based framework for the reconstruction of incomplete data sets. Neurocomputing 73(16–18), 3039–3065 (2010)
Wang, C.Y., Feng, Z.: Boosting with missing predictors. Biostat. (Oxford, England) 11(2), 195 (2010)
An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR. https://www.hindawi.com/journals/jat/2018/2935248/. Accessed 09 Feb 2019
Missing data imputation: focusing on single imputation. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4716933/. Accessed 09 Feb 2019
Zhang, T.: Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the Twenty-First International Conference on Machine learning, ICML 2004, Banff, Alberta, Canada, pp. 116–120 (2004)
Stochastic Gradient Descent for Linear Systems with Missing Data. https://arxiv.org/abs/1702.07098. Accessed 09 Feb 2019
Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybern. SMC-1(4), 364–378 (1971)
Stone-Weierstrass theorem. https://en.wikipedia.org/wiki/Stone%E2%80%93Weierstrass_theorem. Accessed 09 Feb 2019
Vitynskyi, P., Tkachenko, R., Izonin, I., Kutucu, H.: Hybridization of the SGTM neural-like structure through inputs polynomial extension. In: 2018 IEEE Second International Conference on Data Stream Mining and Processing (DSMP), Lviv, Ukraine, pp. 386–391 (2018)
Stochastic Optimization for Machine Learning. http://www.cse.ust.hk/~szhengac/papers/pqe.pdf. Accessed 09 Feb 2019
UCI Machine Learning Repository: Air Quality Data Set. http://archive.ics.uci.edu/ml/datasets/air+quality. Accessed 09 Feb 2019
De Vito, S., Vito, S.D., Massera, E., et al.: On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens. Actuators B: Chem. 129(2), 750–757 (2008)
sklearn.linear_model.SGDRegressor - scikit-learn 0.20.2 documentation. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html. Accessed 09 Feb 2019
Kryvinska, N.: Building Consistent Formal Specification for the Service Enterprise Agility Foundation. J. Serv. Sci. Res. 4(2), 235–269 (2012)
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Izonin, I., Greguš ml., M., Tkachenko, R., Logoyda, M., Mishchuk, O., Kynash, Y. (2019). SGD-Based Wiener Polynomial Approximation for Missing Data Recovery in Air Pollution Monitoring Dataset. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_64
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