一个数据集的风险资本家类型在中国 (1978-2021):一个机器-人混合方法
Jin Chen1, Ruining Cao1, Yifei Song2
1University of Nottingham Ningbo China, Ningbo, China.
Scientific data
|November 20, 2024
概括
本研究使用混合机器学习和人类方法对中国的风险投资者进行了分类. 它确定了七种风险投资类型,为了解中国创业和创新创造了一个有价值的数据库.
科学领域:
- 风险投资和企业家精神
- 创新研究 研究 创新研究
- 中国的经济格局.
背景情况:
- 关于中国的风险投资者 (VC) 类型的研究有限,这是世界上第二大风险投资市场.
- 了解风险投资公司的多样性对于创业和创新研究至关重要.
研究的目的:
- 将中国的风险资本家 (VC) 分为不同的类型.
- 为弥补中国市场关于VC分类的研究缺口.
- 开发一个透明和可扩展的VC类型分类方法.
主要方法:
- 开发了一种混合方法,将机器学习算法和人类编码器结合起来.
- 从CVSource数据库和其他公共来源编制了一套数据集,其中包括在2021年之前在中国投资的49,187家风险投资公司.
- 机器学习模型预测了VC类型,当预测不确定时,人类编码器会介入.
主要成果:
- 风险投资被分为七种不同的类型:一般风险投资 (GVC),企业风险投资 (CVC),独立风险投资 (IVC),银行附属风险投资 (BVC),金融/非银行附属风险投资 (FVC),大学附属风险投资 (UVC) 和养老基金附属风险投资 (PenVC).
- 该研究提供了中国背景下风险投资类型的最新数据库.
- 混合方法证明了透明的VC类型分类的有效性.
结论:
- 开发的混合方法为风险资本家的分类提供了一个强大的方法.
- 创建一个全面的风险投资类型数据库有助于加强对中国创业和创新的研究.
- 利用机器学习与人类专业知识一起,提供了一个可扩展和透明的分类系统.
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