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Empirical models based on machine learning techniques for determining approximate reliability expressions [An article from: Reliability Engineering and System Safety]
Book Details
Author(s)C.M. Rocco S, M. Muselli
PublisherElsevier
ISBN / ASINB000RQZUXE
ISBN-13978B000RQZUX2
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Reliability Engineering and System Safety, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
In this paper two machine learning algorithms, decision trees (DT) and Hamming clustering (HC), are compared in building approximate reliability expression (RE). The main idea is to employ a classification technique, trained on a restricted subset of data, to produce an estimate of the RE, which provides reasonably accurate values of the reliability. The experiments show that although both methods yield excellent predictions, the HC procedure achieves better results with respect to the DT algorithm.
Description:
In this paper two machine learning algorithms, decision trees (DT) and Hamming clustering (HC), are compared in building approximate reliability expression (RE). The main idea is to employ a classification technique, trained on a restricted subset of data, to produce an estimate of the RE, which provides reasonably accurate values of the reliability. The experiments show that although both methods yield excellent predictions, the HC procedure achieves better results with respect to the DT algorithm.
