BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space
Marcell Stippinger,
Dávid Hanák,
Marcell T. Kurbucz,
Gergely Hanczár,
Olivér M. Törteli,
Zoltán Somogyvári
Affiliations
Marcell Stippinger
Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Miklós Street, H-1121 Budapest, Hungary; Corresponding author.
Dávid Hanák
Cursor Insight, 20-22 Wenlock Road, N1 7GU London, United Kingdom
Marcell T. Kurbucz
Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Miklós Street, H-1121 Budapest, Hungary; Institute of Data Analytics and Information Systems, Corvinus University of Budapest, 8 Fővám Square, H-1093, Hungary
Gergely Hanczár
Cursor Insight, 20-22 Wenlock Road, N1 7GU London, United Kingdom
Olivér M. Törteli
Cursor Insight, 20-22 Wenlock Road, N1 7GU London, United Kingdom
Zoltán Somogyvári
Department of Computational Sciences, Wigner Research Centre for Physics, 29-33 Konkoly-Thege Miklós Street, H-1121 Budapest, Hungary
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.
