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

Merkmalsfusion zur Erhöhung der Robustheit von maschinellen Lernern in industriellen Umgebungen
Christoph-Alexander Holst and Volker Lohweg

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.

Funding source: Bundesministerium für Bildung und Forschung

Award Identifier / Grant number: 01IS18041D

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

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Received: 2019-02-28
Accepted: 2019-08-26
Published Online: 2019-09-27
Published in Print: 2019-10-25

© 2019 Walter de Gruyter GmbH, Berlin/Boston

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