Enhancing classification in correlative microscopy using multiple classifier systems with dynamic selection

Samuel Bitrus, Harald Matthias Fitzek, Eugen Rigger, Johannes Rattenberger, Doris Entner

Research output: Contribution to journalArticlepeer-review


Correlative microscopy combines data from different microscopical techniques to gain unique insights about specimens. A key requirement to unlocking the full potential is an advanced classification method that can combine the various analytical signals into physically meaningful phases. The prevalence of highly imbalanced class distributions and high intra-class variability in such real applications makes this a difficult task, yet no study of classifier performance exists in the context of correlative microscopy. This paper investigates the application of both single classifiers as well as multiple classifier systems with dynamic selection. The test sample used for evaluation and comparison of the results is a volcanic rock where data from correlative Raman spectroscopy, Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) are available and prepared for algorithmic evaluation. The results show that multiple classifier systems outperform single classifiers reaching an area under the curve of the receiver operating characteristic of 99% demonstrating the applicability of automated classification in correlative microscopy. Thus, this paper contributes by highlighting the potential of combining the research fields of correlative microscopy and machine learning.
Original languageEnglish
Article number113567
Number of pages11
Publication statusPublished - Oct 2022


  • Classification
  • Correlative microscopy
  • Dynamic selection
  • Machine learning
  • Multiple classifier systems

ASJC Scopus subject areas

  • General Materials Science
  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

Fields of Expertise

  • Advanced Materials Science

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)


Dive into the research topics of 'Enhancing classification in correlative microscopy using multiple classifier systems with dynamic selection'. Together they form a unique fingerprint.

Cite this