Unsupervised Process Monitoring and Fault Diagnosis With Machine Learning Methods 2013
DOI: 10.1007/978-1-4471-5185-2_2
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Overview of Process Fault Diagnosis

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Cited by 2 publications
(2 citation statements)
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“…A random forest (Breiman 2001) is an ensemble of K classification or regression trees that are constructed not only by using different bootstrapped training sets for each tree, but also by restricting the available split variables at each node to randomly drawn input variables (Breiman 2001), as summarised below (Aldrich & Auret 2013): For k = 1 to K (size of ensemble) Construct a bootstrap sample with replacement X k from the learning set X , of the same size as the learning set Grow a random forest tree t k on X k by employing the CART tree growing algorithm, with the following modification at each node: i. Select random input variables from X k to use as possible split variables ii.…”
Section: Textonsmentioning
confidence: 99%
“…A random forest (Breiman 2001) is an ensemble of K classification or regression trees that are constructed not only by using different bootstrapped training sets for each tree, but also by restricting the available split variables at each node to randomly drawn input variables (Breiman 2001), as summarised below (Aldrich & Auret 2013): For k = 1 to K (size of ensemble) Construct a bootstrap sample with replacement X k from the learning set X , of the same size as the learning set Grow a random forest tree t k on X k by employing the CART tree growing algorithm, with the following modification at each node: i. Select random input variables from X k to use as possible split variables ii.…”
Section: Textonsmentioning
confidence: 99%
“…O valor de concentração dessa amostra retirada é prevista e a raiz quadrada do erro quadrático de validação cruzada (RMSECV) é encontrada. Essa é uma característica importante dos algoritmos de árvores de decisão, que são um conjunto de regras de if-then-else [37], de programação, no qual uma decisão é tomada localmente para se obter um resposta final mais interpretável. Por fim, em árvores de decisão as probabilidades iniciais se alteram a medida que mais informação é obtida pelo sistema, seguindo a chamada regra de Bayes:…”
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