微型电脑应用2023，Vol.39Issue(12)：12-15,4.
基于机器学习的迭代式数据均衡分区算法研究
Research on Iterative Data Balance Partitioning Algorithm Based on Machine Learning
摘要
Abstract
To achieve data balanced partitioning,a machine learning-based iterative data balanced partitioning algorithm is pro-posed.The central data warehousing technique is used to integrate the data and simultaneously perform data cleaning and re-duction.The kernel principal component analysis method is applied to extract features from the integrated data.Based on the feature extraction results,a decision tree algorithm in machine learning is employed to construct a classifier model.The itera-tive data balanced partitioning is achieved.The results indicate that the proposed algorithm achieves a data recall rate of over 90％and an average precision rate of over 85％,which is superior to traditional methods.关键词
数据均衡分区/机器学习/迭代式/核主成分分析/决策树Key words
data balanced partitioning/machine learning/iterative/kernel principal component analysis/decision tree分类
信息技术与安全科学引用本文复制引用
张镝,吴宇强..基于机器学习的迭代式数据均衡分区算法研究[J].微型电脑应用,2023,39(12):12-15,4.基金项目
吉林省教育厅科学研究项目(JJKH20231542KJ) （JJKH20231542KJ）
