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Supervised learning for the analysis of process operational data

delete2000-07-01
delete12
PRE
AI
Y
Yoshiyuki Yamashita
DOI：10.1016/S0098-1354(00)00497-Xdelete
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Abstract

Abstract

En 中文
For the extraction of useful knowledge from recorded process operational data, several data mining algorithms are examined on a data set generated by a dynamic simulator of a debutanizer plant. Decision tree inducer can directly extract reasonable operational rules from the data-set with no previous knowledge. By integrating the feature-subset selection wrapper algorithm, Naive-Bayes classifier and nearest-neighbor classifier can also estimate the action of operation successfully. (C) 2000 Elsevier Science Ltd. All rights reserved.
Keywords:
machine learning
classification
operational knowledge
data mining
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

C
Computers and Chemical Engineering
IF:
3.9
Papers:
8.1K
Citations:
1.7W

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