Simulation of calibrated complex synthetic population data with XGBoost
Autor:in (Körperschaft)
Publikationsdatum
2024
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Algorithms
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
17
Ausgabe / Nummer
6
Seiten / Dauer
Patentnummer
Verlag / Herausgebende Institution
MDPI
Verlagsort / Veranstaltungsort
Basel
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Synthetic data generation methods are used to transform the original data into privacy-compliant synthetic copies (twin data). With our proposed approach, synthetic data can be simulated in the same size as the input data or in any size, and in the case of finite populations, even the entire population can be simulated. The proposed XGBoost-based method is compared with known model-based approaches to generate synthetic data using a complex survey data set. The XGBoost method shows strong performance, especially with synthetic categorical variables, and outperforms other tested methods. Furthermore, the structure and relationship between variables are well preserved. The tuning of the parameters is performed automatically by a modified k-fold cross-validation. If exact population margins are known, e.g., cross-tabulated population counts on age class, gender and region, the synthetic data must be calibrated to those known population margins. For this purpose, we have implemented a simulated annealing algorithm that is able to use multiple population margins simultaneously to post-calibrate a synthetic population. The algorithm is, thus, able to calibrate simulated population data containing cluster and individual information, e.g., about persons in households, at both person and household level. Furthermore, the algorithm is efficiently implemented so that the adjustment of populations with many millions or more persons is possible.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
005 - Computer Programmierung, Programme und Daten
005 - Computer Programmierung, Programme und Daten
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1999-4893
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
GUSSENBAUER, Johannes, Matthias TEMPL, Siro FRITZMANN und Alexander KOWARIK, 2024. Simulation of calibrated complex synthetic population data with XGBoost. Algorithms. 2024. Bd. 17, Nr. 6. DOI 10.3390/a17060249. Verfügbar unter: https://doi.org/10.26041/fhnw-11042