BiometricBlender : Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space
  • Metaadatok
Tartalom: https://unipub.lib.uni-corvinus.hu/10434/
Archívum: Corvinus Kutatások
Gyűjtemény: Status = Published
Subject = Computer science
Subject = Automatizálás, gépesítés
Type = Article
Cím:
BiometricBlender : Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space
Létrehozó:
Stippinger, Marcell
Hanák, Dávid
Kurbucz, Marcell Tamás
Hanczár, G.
Törteli, O.M.
Somogyvári, Zoltán
Kiadó:
Elsevier
Dátum:
2023
Téma:
Automatizálás, gépesítés
Computer science
Tartalmi leírás:
The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially in the field of biometrics, where the usage of such datasets is common. This paper reports a Python package called BiometricBlender, which is an ultra-high dimensional, multi-class synthetic data generator to benchmark a wide range of feature screening methods. During the data generation process, the overall usefulness and the intercorrelations of blended features can be controlled by the user, thus the synthetic feature space is able to imitate the key properties of a real biometric dataset.
Nyelv:
angol
angol
Típus:
Article
PeerReviewed
Formátum:
application/pdf
Azonosító:
Stippinger, Marcell ORCID: https://orcid.org/0000-0002-9954-8089 <https://orcid.org/0000-0002-9954-8089>, Hanák, Dávid, Kurbucz, Marcell Tamás ORCID: https://orcid.org/0000-0002-0121-6781 <https://orcid.org/0000-0002-0121-6781>, Hanczár, G., Törteli, O.M. and Somogyvári, Zoltán (2023) BiometricBlender : Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space. Softwarex, 22 . DOI 10.1016/j.softx.2023.101366 <https://doi.org/10.1016/j.softx.2023.101366>
Kapcsolat:
10.1016/j.softx.2023.101366