Temat: machine set - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Application of machine learning and rough set theory in lean maintenance decision support system development
Autorzy:: Antosz, Katarzyna
Jasiulewicz-Kaczmarek, Małgorzata
Paśko, Łukasz
Zhang, Chao
Wang, Shaoping
Powiązania:: https://bibliotekanauki.pl/articles/2038009.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:: lean maintenance
availability
machine learning
decision trees
rough set theory
Opis:: Lean maintenance concept is crucial to increase the reliability and availability of maintenance equipment in the manufacturing companies. Due the elimination of losses in maintenance processes this concept reduce the number of unplanned downtime and unexpected failures, simultaneously influence a company’s operational and economic performance. Despite the widespread use of lean maintenance, there is no structured approach to support the choice of methods and tools used for the maintenance function improvement. Therefore, in this paper by using machine learning methods and rough set theory a new approach was proposed. This approach supports the decision makers in the selection of methods and tools for the effective implementation of Lean Maintenance.
Źródło:: Eksploatacja i Niezawodność; 2021, 23, 4; 695-708
1507-2711
Pojawia się w:: Eksploatacja i Niezawodność
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Specialized, MSE-optimal m-estimators of the rule probability especially suitable for machine learning
Autorzy:: Piegat, A.
Landowski, M.
Powiązania:: https://bibliotekanauki.pl/articles/205508.pdf
Data publikacji:: 2014
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:: machine learning
rule probability
probability estimation
m-estimators
decision trees
rough set theory
Opis:: The paper presents an improved sample based rule- probability estimation that is an important indicator of the rule quality and credibility in systems of machine learning. It concerns rules obtained, e.g., with the use of decision trees and rough set theory. Particular rules are frequently supported only by a small or very small number of data pieces. The rule probability is mostly investigated with the use of global estimators such as the frequency-, the Laplace-, or the m-estimator constructed for the full probability interval [0,1]. The paper shows that precision of the rule probability estimation can be considerably increased by the use of m-estimators which are specialized for the interval [phmin, phmax] given by the problem expert. The paper also presents a new interpretation of the m-estimator parameters that can be optimized in the estimators.
Źródło:: Control and Cybernetics; 2014, 43, 1; 133-160
0324-8569
Pojawia się w:: Control and Cybernetics
Dostawca treści:: Biblioteka Nauki

Artykuł

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