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.
翻译:缺乏可自由获取(实时或合成)高或超高维、多级多级数据集,可能妨碍对地物筛选的迅速增长的研究,特别是在生物鉴别学领域,因为生物鉴别学领域使用这类数据集很常见。本文报告了一个称为生物测量Blender的Python包件,这是一个超高维、多级合成数据生成器,用以衡量各种地物筛选方法的基准。在数据生成过程中,用户可以控制混合地物的总体用途和相互关系,因此合成地物空间能够模仿实际生物鉴别数据集的关键特性。