Abstract
This paper presents the use of random forests for knowledge discovery from large data sets. The data was obtained from sensors of glassworks technological process monitoring system. Based on original and standardized data, several random forest models were created. Then they were used to establish a significance ranking of explanatory variables. The ranking presents information about explanatory variables that most affect the product defects.
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References
Piatek, Z.: http://przemysl-40.pl/index.php/2017/03/22/czym-jest-przemysl-4-0/. Accessed 9 Jan 2019
Harding, J., Shahbaz, M., Kusiak, A.: Data mining in manufacturing: a review. J. Manuf. Sci. Eng. 128(4), 969–976 (2006). https://doi.org/10.1115/1.2194554
Cichosz, P.: Systemy uczace sie. WNT, Warszawa (2000)
Flach, P.: Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge University Press, Cambridge (2012)
Gama, J.: Knowledge Discovery from Data Streams, 1st edn. Chapman & Hall/CRC, Boca Raton (2010)
Setlak, G., Pirog-Mazur, M., Pasko, L.: Intelligent analysis of manufacturing data. In: Setlak, G., Markov, K. (eds.) Computational Models for Business and Engineering Domains. ITHEA, pp. 109–122. Rzeszow-Sofia (2014)
Larose, D.T.: Odkrywanie wiedzy z danych. PWN, Warszawa (2006)
Morzy, T.: Eksploracja danych. Metody i algorytmy. PWN, Warszawa (2013)
Osowski, S.: Metody i narzędzia eksploracji danych. Wydawnictwo BTC, Legionowo (2013)
Sayad, S.: http://www.saedsayad.com/. Accessed 9 Jan 2019
Saad, H.: The application of data mining in the production processes. Ind. Eng. 2(1), 26–33 (2018)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Liaw, A., Wiener, M.: Classification and regression by random forest. R News 2(3), 18–22 (2002)
Gorska, R., Staszkiewicz, P.: Zastosowanie algorytmu lasow losowych do prognozowania modyfikacji opinii bieglego rewidenta. J. Manag. Financ. 15(3), 339–348 (2017)
Accorsi, R., Manzini, R., Pascarella, P., Patella, M., Sassi, S.: Data mining and machine learning for condition-based maintenance. Procedia Manuf. 11, 1153–1161 (2017)
Komal Kumar, N., Vigneswari, D., Vamsi Krishna, M., Phanindra Reddy, G.V.: An optimized random forest classifier for diabetes mellitus. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol. 813. Springer, Singapore (2019)
Jo, T., Cheng, J.: Improving protein fold recognition by random forest. BMC Bioinform. 15, S14 (2014)
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Setlak, G., Pasko, L. (2020). Random Forests in a Glassworks: Knowledge Discovery from Industrial Data. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019. ISAT 2019. Advances in Intelligent Systems and Computing, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-30604-5_16
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DOI: https://doi.org/10.1007/978-3-030-30604-5_16
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