一个GAN-BO-XGBoost模型用于高质量的专利识别
Zengyuan Wu1, Jiali Zhao2, Ying Li2
1College of Economics and Management, China Jiliang University, No. 258, Xueyuan Street, Hangzhou, 310018, Zhejiang, People's Republic of China. wuzengyuan@cjlu.edu.cn.
Scientific reports
|April 26, 2024
概括
识别高质量的专利至关重要,但由于其普及率较低,因此具有挑战性. 我们开发了一个GAN-BO-XGBoost模型,增强专利分析,以便更好地做出研发决策.
科学领域:
- 知识产权管理知识产权管理
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 专利申请的快速增长使得区分高质量的专利和低质量的专利成为一个挑战.
- 准确识别高质量的专利对于明智的研发决策和战略专利组合管理至关重要.
- 高品质专利的稀缺性使得它们的有效识别成为一个重大障碍.
研究的目的:
- 开发一个改进的模型,以有效和准确地识别高质量的专利.
- 通过结合新的功能来增强现有的专利质量评估系统.
- 解决专利质量分类中数据集不平衡的挑战.
主要方法:
- 通过增加与专利权人的技术实力相关的四个特征来重建专利质量指数系统.
- 整合重新采样技术,特别是生成对抗网络 (GAN),以扩大少数 (高质量专利) 样本.
- 应用一个集体学习算法,极端梯度提升 (XGBoost),优化与贝叶斯优化 (BO),形成GAN-BO-XGBoost模型.
主要成果:
- 拟议的GAN-BO-XGBoost模型在识别高质量的专利方面,与其他模型相比,表现出更高的性能和稳定性.
- 使用石版技术领域的专利数据进行的评估和十倍交叉验证证实了该模型的有效性.
- 整合GAN用于数据增强和BO-XGBoost用于分类,显著提高了识别准确性.
结论:
- GAN-BO-XGBoost模型为准确有效地识别高质量专利提供了强大而有效的解决方案.
- 这种方法提高了组织做出战略研发决策和优化专利布局的能力.
- 该研究强调了结合先进的机器学习技术来应对知识产权分析方面的挑战的潜力.
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