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at - Automatisierungstechnik

Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik

[AT - Automation Technology: Methods and Applications of Control, Regulation, and Information Technology
]

Editor-in-Chief: Jumar, Ulrich


IMPACT FACTOR 2018: 0.500

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Volume 67, Issue 10

Issues

Feature fusion to increase the robustness of machine learners in industrial environments

Merkmalsfusion zur Erhöhung der Robustheit von maschinellen Lernern in industriellen Umgebungen

M. Sc. Christoph-Alexander Holst
  • Corresponding author
  • inIT – Institute Industrial IT, Technische Hochschule Ostwestfalen-Lippe, Campusallee 6, 32657 Lemgo, Germany
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Prof. Dr.-Ing. Volker Lohweg
Published Online: 2019-09-27 | DOI: https://doi.org/10.1515/auto-2019-0028

Abstract

Industrial applications put special demands on machine learning algorithms. Noisy data, outliers, and sensor faults present an immense challenge for learners. A considerable part of machine learning research focuses on the selection of relevant, non-redundant features. This contribution details an approach to group and fuse redundant features prior to learning and classification. Features are grouped relying on a correlation-based redundancy measure. The fusion of features is guided by determining the majority observation based on possibility distributions. Furthermore, this paper studies the effects of feature fusion on the robustness and performance of classification with a focus on industrial applications. The approach is statistically evaluated on public datasets in comparison to classification on selected features only.

Zusammenfassung

Industrielle Anwendungen stellen besondere Anforderungen an maschinelle Lernalgorithmen. Verrauschte Daten, Ausreißer und Sensorfehler sind eine große Herausforderung für Lerner. Ein erheblicher Teil der Forschung im Bereich maschinelles Lernen konzentriert sich auf die Auswahl relevanter, nicht redundanter Merkmale. Dieser Beitrag beschreibt einen Ansatz zur Gruppierung und Fusion redundanter Merkmale, die vor dem Lernen und Klassifizieren ausgeführt werden. Die Gruppierung der Merkmale basiert auf einer korrelationsbasierten Redundanzmessung, während die Fusionierung von Merkmalen auf der Bestimmung einer über Möglichkeitsverteilungen ermittelten Mehrheitsbeobachtung basiert. Darüber hinaus untersucht dieser Beitrag die Auswirkungen der Merkmalsfusion auf die Robustheit und Leistungsfähigkeit der Klassifizierung. Der Ansatz wird anhand öffentlicher Datensätze im Vergleich zur Klassifizierung auf ausgewählten Merkmalen statistisch ausgewertet.

Keywords: robust machine learning; feature grouping; feature fusion; information fusion

Schlagwörter: Robustes Maschinelles Lernen; Merkmalsgruppierung; Merkmalsfusion; Informationsfusion

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About the article

M. Sc. Christoph-Alexander Holst

Christoph-Alexander Holst received his master’s degree in information technology from the Technische Hochschule Ostwestfalen-Lippe, Germany. He is working towards his doctoral degree in cooperation with the Computer Engineering Group at the Brandenburg University of Technology Cottbus-Senftenberg. His main research topics are information fusion, fusion system design, and sensor orchestration.

Prof. Dr.-Ing. Volker Lohweg

Volker Lohweg is director of the inIT – Institute Industrial IT and head of the research group Discrete Systems. The research group’s working area is dedicated to cognitive systems in automation especially information fusion in the context of intelligent technical systems. He is active in SPIE and IEEE as a reviewer in the field of image processing and data analysis. His actual interests are sensory conflict modelling and multi-scale signal analysis.


Received: 2019-02-28

Accepted: 2019-08-26

Published Online: 2019-09-27

Published in Print: 2019-10-25


Funding Source: Bundesministerium für Bildung und Forschung

Award identifier / Grant number: 01IS18041D

This work was partly funded by the German Federal Ministry of Education and Research within the project ITS.ML, Grant number 01IS18041D.


Citation Information: at - Automatisierungstechnik, Volume 67, Issue 10, Pages 853–865, ISSN (Online) 2196-677X, ISSN (Print) 0178-2312, DOI: https://doi.org/10.1515/auto-2019-0028.

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