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A fingerprint of a heterogeneous data set

Author

Listed:
  • Matteo Spallanzani

    (ETH Zürich)

  • Gueorgui Mihaylov

    (GlaxoSmithKline
    King’s College London)

  • Marco Prato

    (Università di Modena e Reggio Emilia)

  • Roberto Fontana

    (Politecnico di Torino)

Abstract

In this paper, we describe the fingerprint method, a technique to classify bags of mixed-type measurements. The method was designed to solve a real-world industrial problem: classifying industrial plants (individuals at a higher level of organisation) starting from the measurements collected from their production lines (individuals at a lower level of organisation). In this specific application, the categorical information attached to the numerical measurements induced simple mixture-like structures on the global multivariate distributions associated with different classes. The fingerprint method is designed to compare the mixture components of a given test bag with the corresponding mixture components associated with the different classes, identifying the most similar generating distribution. When compared to other classification algorithms applied to several synthetic data sets and the original industrial data set, the proposed classifier showed remarkable improvements in performance.

Suggested Citation

  • Matteo Spallanzani & Gueorgui Mihaylov & Marco Prato & Roberto Fontana, 2022. "A fingerprint of a heterogeneous data set," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 617-657, September.
    • Handle: RePEc:spr:advdac:v:16:y:2022:i:3:d:10.1007_s11634-021-00452-9
    DOI: 10.1007/s11634-021-00452-9
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