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Maximizing information from chemical engineering data sets: Applications to machine learning. (arXiv:2201.10035v1 [stat.ML])
cs.LG updates on arXiv.org arxiv.org
It is well-documented how artificial intelligence can have (and already is
having) a big impact on chemical engineering. But classical machine learning
approaches may be weak for many chemical engineering applications. This review
discusses how challenging data characteristics arise in chemical engineering
applications. We identify four characteristics of data arising in chemical
engineering applications that make applying classical artificial intelligence
approaches difficult: (1) high variance, low volume data, (2) low variance,
high volume data, (3) noisy/corrupt/missing data, and (4) restricted …
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