An alternative approach to dimension reduction for pareto distributed data: a case study
Marco Roccetti; Giovanni Delnevo; Luca Casini; Silvia Mirri · 2021 · Journal Of Big Data
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the
Abstract by Marco Roccetti; Giovanni Delnevo; Luca Casini; Silvia Mirri, Journal Of Big Data (2021) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s40537-021-00428-8