A fingerprint of a heterogeneous data set

Matteo Spallanzani (), Gueorgui Mihaylov, Marco Prato and Roberto Fontana
Additional contact information
Matteo Spallanzani: ETH Zürich
Gueorgui Mihaylov: GlaxoSmithKline
Marco Prato: Università di Modena e Reggio Emilia
Roberto Fontana: Politecnico di Torino

Advances in Data Analysis and Classification, 2022, vol. 16, issue 3, No 6, 617-657

Abstract: 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.

Keywords: Bagged data; Mixed-type data; Mixture distributions; Multivariate statistics; Machine learning; 62P30; 62R07; 62H12; 62H30 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11634-021-00452-9

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