Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "naïve Bayesian classifier" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Approach to predict product quality considering current customers’ expectations
Autorzy:
Siwiec, Dominika
Pacana, Andrzej
Bednárová, Lucia
Powiązania:
https://bibliotekanauki.pl/articles/27313593.pdf
Data publikacji:
2022
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
predict
product quality
decision support
naïve Bayesian classifier
weighted sum model
customer expectations
przewidywanie
jakość produktu
wspomaganie decyzji
naiwny klasyfikator Bayesa
metoda sumy ważonej
oczekiwania klientów
Opis:
Purpose: The purpose was to develop an approach to predict product quality considering current customers' expectations. Design/methodology/approach: The approach includes integrated techniques, i.e.: SMART(-ER) method, a questionnaire with the Likert scale, brainstorming (B&M), WSM method, and Naïve Bayes Classifier. This approach refers to obtaining customers' expectations for satisfaction from the current quality of products and the importance of these criteria. Based on the satisfaction of customers, the quality of the product was estimated and classified. Then, the quality of the product was predicted for current customers. Findings: It was shown that it is possible to predict product quality based on current customer expectations, and so based on the current existing product. Research limitations/implications: The proposed approach does not include the possibilities of determining the expected quality of the product. The approach focuses on predicting customers' satisfaction with the current quality of the product. Therefore, if there is a need for improvement actions, further analyzes should be carried out to determine which criteria should be modified and how. Practical implications: The presented approach can be used for any product. Therefore, it is a useful tool for any kind of organization, which strives to meet customer satisfaction. Despite the possibility to predict the quality of the product, the proposed approach can indicate at an early stage to the organization that it is necessary to make improvement actions. Social implications: It is possible to reduce the waste of resources by predicting that improvement actions are necessary. Moreover, the approach supports an entity (e.g., expert, enterprise, interested parties) in predicting current customers' satisfaction. Originality/value: Originality is predicting product quality based on current customers' expectations. A new combination of quality management techniques, decision support, and machine learning was implemented.
Źródło:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2022, 155; 461--472
1641-3466
Pojawia się w:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Time-Series Analysis for Predicting Defects in Continuous Steel Casting Process
Autorzy:
Rodziewicz, A.
Perzyk, M.
Powiązania:
https://bibliotekanauki.pl/articles/380643.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
information technology
foundry industry
quality management
continuous steel casting
time series analysis
naïve Bayesian classifier
technologia informatyczna
przemysł odlewniczy
zarządzanie jakością
ciągłe odlewanie stali
analiza szeregów czasowych
naiwny klasyfikator Bayesa
Opis:
The purpose of this paper was testing suitability of the time-series analysis for quality control of the continuous steel casting process in production conditions. The analysis was carried out on industrial data collected in one of Polish steel plants. The production data concerned defective fractions of billets obtained in the process. The procedure of the industrial data preparation is presented. The computations for the time-series analysis were carried out in two ways, both using the authors’ own software. The first one, applied to the real numbers type of the data has a wide range of capabilities, including not only prediction of the future values but also detection of important periodicity in data. In the second approach the data were assumed in a binary (categorical) form, i.e. the every heat(melt) was labeled as ‘Good’ or ‘Defective’. The naïve Bayesian classifier was used for predicting the successive values. The most interesting results of the analysis include good prediction accuracies obtained by both methodologies, the crucial influence of the last preceding point on the predicted result for the real data time-series analysis as well as obtaining an information about the type of misclassification for binary data. The possibility of prediction of the future values can be used by engineering or operational staff with an expert knowledge to decrease fraction of defective products by taking appropriate action when the forthcoming period is identified as critical.
Źródło:
Archives of Foundry Engineering; 2016, 16, 4; 125-130
1897-3310
2299-2944
Pojawia się w:
Archives of Foundry Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning the naive Bayes classifier with optimization models
Autorzy:
Taheri, S.
Mammadov, M.
Powiązania:
https://bibliotekanauki.pl/articles/908351.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
Bayesian networks
naive Bayes classifier
optimization
discretization
sieci bayesowskie
naiwny klasyfikator Bayesa
optymalizacja
dyskretyzacja
Opis:
Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 787-795
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies