Abstract
In the paper, a new method for solving the multiple linear regression task via a linear polynomial as a constructive formula is proposed. It is based on the use of high-speed SGTM Neural-Like Structure. This linear non-iterative computational intelligence tool is used for identification of polynomial coefficients. As a result of the implementation of the learning algorithm and applied the matrix of test signals to the trained SGTM, the identification of the linear polynomial coefficients is carried out. A further solution of the task occurs by searching a dependent variable using the obtained polynomial. The results of the method have been tested on the task of the output of the electric power prediction of the combined-type factory. The method ensures the identification of five polynomial’s coefficients at the high speed, which ensures high accuracy of the solution. Based on the comparison with known regression analysis methods, the highest accuracy of the work has been established. The transition from neural-like structure to the solution of the task in the form of a linear polynomial provides the possibility for the simple interpretation of the result of the regression or classification tasks. That does not require high qualifications from the user. In addition, the developed method, based on the repetition of training outcomes and the lack of debugging and parameter selection procedures, allows synthesizing linear polynomial for complex models that use various non-linear extensions of SGTM inputs while preserving the accuracy of their operation. The proposed approach can be used in the fields of medicine, economics, materials science, service sciences etc., for fast and accurate solution of regression or classification tasks with the possibility of easy interpretation of the result.
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References
Berk, R.A.: Data mining within a regression framework. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 231–255. Springer, Boston (2005). https://doi.org/10.1007/0-387-25465-X_11
Rathi, M.: Regression modeling technique on data mining for prediction of CRM. In: Das, V.V., Vijaykumar, R. (eds.) ICT 2010. CCIS, vol. 101, pp. 195–200. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15766-0_28
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
Tkachenko, R., Izonin, I.: Model and principles for the implementation of neural-like structures based on geometric data transformations. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2018. AISC, vol. 754, pp. 578–587. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91008-6_58
Arouri, C., Nguifo, E.M., Aridhi, S., et al.: Towards a constructive multilayer perceptron for regression task using non-parametric clustering. A case study of Photo-Z redshift reconstruction (2019). https://arxiv.org/abs/1412.5513
Alomair, O.A., Garrouch, A.A.: A general regression neural network model offers reliable prediction of CO2 minimum miscibility pressure. J. Petrol. Explor. Prod. Technol. 6(3), 351–365 (2016)
Song, J., Romero, C.E., Yao, Z., He, B.: A globally enhanced general regression neural network for online multiple emissions prediction of utility boiler. Knowl. Based Syst. 118(C), 4–14 (2017)
Multi-target support vector regression via correlation regressor chains (2019). https://www.sciencedirect.com/science/article/pii/S0020025517307946
Tkachenko, R., Izonin, I., Vitynskyi, P., Lotoshynska, N., Pavlyuk, O.: Development of the non-iterative supervised learning predictor based on the ito decomposition and SGTM neural-like structure for managing medical insurance costs. Data 3(4), 46 (2018)
Doroshenko, A.: Piecewise-linear approach to classification based on geometrical transformation model for imbalanced dataset. In: 2018 IEEE Second International Conference on Data Stream Mining Processing (DSMP), pp. 231–235 (2018)
Boyko, N., Sviridova, T., Shakhovska, N.: Use of machine learning in the forecast of clinical consequences of cancer diseases. In: 2018 7th Mediterranean Conference on Embedded Computing (MECO), Budva, 2018, pp. 1–6 (2018)
UCI Machine Learning Repository: Combined Cycle Power Plant Data Set (2019). https://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant
Hassan, A.H.A., Elfaki, E.: Prediction of electrical output power of combined cycle power plant using regression ANN model. Int. J. Comput. Sci. Control Eng. 6(2), 9–21 (2018)
Demšar, J., et al.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14, 2349–2353 (2013)
Fedushko, S., Ustyianovych, T.: Predicting pupil’s successfulness factors using machine learning algorithms and mathematical modelling methods. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2019. AISC, vol. 938, pp. 625–636. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-16621-2_58
Kazarian, A., Teslyuk, V., Tsmots, I., Mashevska, M.: Units and structure of automated ‘smart’ house control system using machine learning algorithms. In: 2017 14th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), pp. 364–366 (2017)
Scikit-learn Machine Learning in Python (2019). http://scikit-learn.org/stable/
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Izonin, I., Tkachenko, R., Kryvinska, N., Tkachenko, P., Greguš ml., M. (2019). Multiple Linear Regression Based on Coefficients Identification Using Non-iterative SGTM Neural-like Structure. 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_39
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