The Point Nuisance Method as a decision-support system based on bayesian inference approach

• Autorzy: Rusek Janusz

Identyfikatory

DOI

10.24425/ams.2020.132710

• Języki publikacji: EN

Abstrakty

EN

The article attempts to transfer information from the Point Nuisance Method (PNM) used in Poland in the issue of protection of buildings in mining areas, to the system of inference based on Bayesian formalism. For this purpose, all possible combinations occurring in PNM were selected. The number of numerically generated patterns was 6,718,464 cases. Then, based on Python package Scikit-Learn, a classification model was created in the form of the Naïve Bayes Classifier (NBC). The effectiveness of three methods used to build this type of decision-support system was analysed, from which the Categorical Multinomial Naive Bayes (CMNB) approach was finally selected. With the created classifier, its properties were verified in terms of quality of classify and generalization. For this purpose a general approach was used, analysing the level of accuracy of the model in relation to training and teaching data, and detailed, based on the analysis of the confusion matrix. Additionally, the operation of the created classifier was simulated to determine the optimal Laplace smoothing parameter α. The article ends with conclusions from the carried out calculations, in which an attempt was made to answer the question concerning potential reasons for incorrect classification of the created CMNB model. The discussion ends with a reference to the planned research, in which, among other things, the use of more complex Bayesian belief networks (BBN) is planned.

Słowa kluczowe

EN

resistance of buildings; mining area; reliability; surface deformations

PL

odporność budynku; obszar wydobycia; niezawodność; deformacja powierzchni

• Wydawca: Instytut Mechaniki Górotworu PAN
• Czasopismo: Archives of Mining Sciences
• Rocznik: 2020
• Tom: Vol. 65, no. 1
• Strony: 117--127
• Opis fizyczny: Bibliogr. 21 poz., rys., tab., wykr.

Twórcy

autor

Rusek Janusz

• AGH University of Science and Technology, Faculty of Mining Surveying and Environmental Engineering, Department of Engineering Surveying and Civil Engineering, al. Mickiewicza 30, 30-059 Krakow, Poland

Bibliografia

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• [18] J. Rusek, Support vector machines and probabilistic neural networks in the assessment of the risk of damage to water supply systems in mining areas. 2016.
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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020)