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Erschienen in:
Processing Nonlinearities Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

19. Improving the Stability of Variable Selection for Industrial Datasets

verfasst von : Silvia Cateni, Valentina Colla, Vincenzo Iannino

Erschienen in: Neural Advances in Processing Nonlinear Dynamic Signals

Verlag: Springer International Publishing

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Abstract

Variable reduction is an essential step in data mining, which is able effectively to increase both the performance of machine learning and the process knowledge by removing the redundant and irrelevant input variables. The paper presents a variable selection approach merging the dominating set procedure for redundancy analysis and a wrapper approach in order to achieve an informative and not redundant subset of variables improving both the stability and the computational complexity. The proposed approach is tested on different datasets coming from the UCI repository and from industrial contexts and is compared to the exhaustive variable selection approach, which is often considered optimal in terms of system performance. Moreover the novel method is applied to both classification and regression procedures.

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Vorheriges Kapitel Appraisal of Enhanced Surrogate Models for Substrate Integrate Waveguide Devices Characterization
Nächstes Kapitel Cause and Effect Analysis in a Real Industrial Context: Study of a Particular Application Devoted to Quality Improvement
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Metadaten
Titel
Improving the Stability of Variable Selection for Industrial Datasets
verfasst von
Silvia Cateni
Valentina Colla
Vincenzo Iannino
Copyright-Jahr
2019
Buch
Neural Advances in Processing Nonlinear Dynamic Signals

Print ISBN: 978-3-319-95097-6

Electronic ISBN: 978-3-319-95098-3

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-95098-3_19