摘要
针对复杂产品关键质量特征特性识别中存在的数据高维度、小样本、不平衡问题,提出一种基于Bootstrap K-split Lasso(B-K-Lasso)的关键质量特征识别方法,并采用UCI数据库中的SECOM数据集进行仿真实验。结果表明:该方法能够有效识别复杂产品的关键质量特性,并且对不合格品的识别率显著提升。
In order to solve the problems of high dimensions, small sample size and unbalanced data in the recognition of critical-to-quanlity of complex products, this paper proposes a critical-to-quanlity recognition method based on Bootstrap K-split Lasso(B-K-split Lasso). The simulation results of SECOM data set in UCI database show that this method can effectively identify the critical-to-quanlity of complex products and significantly improve the accuracy of nonconforming products.
作者
李秀
LI Xiu(Business School of Zhengzhou University,Zhengzhou 450001)
出处
《现代制造技术与装备》
2022年第1期218-221,共4页
Modern Manufacturing Technology and Equipment
