Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Zeitschriftenartikel

Recurrence microstates for machine learning classification

Urheber*innen

Spezzatto,  G. S.
External Organizations;

Flauzino,  J. V. V.
External Organizations;

Corso,  G.
External Organizations;

Boaretto,  B. R. R.
External Organizations;

Macau,  E. E. N.
External Organizations;

Prado,  T. L.
External Organizations;

/persons/resource/lopes.sergio

Lopes,  Sergio Roberto
Potsdam Institute for Climate Impact Research;

Externe Ressourcen
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PIKpublic verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Spezzatto, G. S., Flauzino, J. V. V., Corso, G., Boaretto, B. R. R., Macau, E. E. N., Prado, T. L., Lopes, S. R. (2024): Recurrence microstates for machine learning classification. - Chaos, 34, 7, 073140.
https://doi.org/10.1063/5.0203801


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_31350
Zusammenfassung
Recurrence microstates are obtained from the cross recurrence of two sequences of values embedded in a time series, being the generalization of the concept of recurrence of a given state in phase space. The probability of occurrence of each microstate constitutes a recurrence quantifier. The set of probabilities of all microstates are capable of detecting even small changes in the data pattern. This creates an ideal tool for generating features in machine learning algorithms. Thanks to the sensitivity of the set of probabilities of occurrence of microstates, it can be used to feed a deep neural network, namely, a microstate multi-layer perceptron (MMLP) to classify parameters of chaotic systems. Additionally, we show that with more microstates, the accuracy of the MMLP increases, showing that the increasing size and number of microstates insert new and independent information into the analysis. We also explore potential applications of the proposed method when adapted to different contexts.