软件导刊2019，Vol.18Issue(1)：124-127,4.DOI:10.11907/rjdk.181535
基于集成学习算法的工业产品质量预测
Quality Prediction in Industrial Products Using Ensemble Methods
摘要
Abstract
Huge amounts of data in manufacturing, assembly and testing are generated and stored in modern digitalized industrial production.The process data contains inherent knowledge and information, which determines the final quality that needs to be extracted by data mining.In the process of detecting product quality, it is usually late to fix after finding products with poor quality.In data mining, by using process data we can predict product quality in advance and make modifications.In this paper, by using CRISP-DM methodology and ensemble methods (Random Forests and XGBoost) , we made a precise quality regression and classification prediction.Accurate optimized models are gained after parameter tuning.These models would be beneficial for improving product quality in practice.关键词
数据挖掘/CRISP-DM/质量预测/集成学习/随机森林/XGboostKey words
data mining/CRISP-DM/quality prediction/ensemble learning/random forests/XGBoost分类
信息技术与安全科学引用本文复制引用
江琨,丁学明..基于集成学习算法的工业产品质量预测[J].软件导刊,2019,18(1):124-127,4.基金项目
国家自然科学基金项目(11502145) （11502145）
