计算机应用研究2024，Vol.41Issue(6)：1743-1748,6.DOI:10.19734/j.issn.1001-3695.2023.10.0523
面向简化规则的集成学习模型及规则约简策略
Research on ensemble learning model for simplified rules and rule reduction strategy
摘要
Abstract
With the widespread application of machine learning models,researchers have gradually recognized the limitations of such methods.Most of these models are black-box models,resulting in poor interpretability.To address this issue,this pa-per proposed a rule-based interpretable model and rule reduction method based on ensemble learning models,which included generating optimized random forest models,discovering and reducing redundant rules,and other steps.Firstly,this paper pro-posed an evaluation method for random forest models,and optimized the key parameters of random forest models based on the idea of reinforcement learning,resulting in a more interpretable random forest model.Secondly,the rule sets extracted from the random forest model were subjected to redundancy elimination,resulting in a more concise rule set.Experimental results on public datasets show that the generated rule sets perform well in terms of prediction accuracy and interpretability.关键词
可解释模型/规则学习/集成学习/规则约简Key words
interpretable model/rule learning/ensemble learning/rule reduction分类
信息技术与安全科学引用本文复制引用
张纬之,韩珣,谢志伟,石胜飞..面向简化规则的集成学习模型及规则约简策略[J].计算机应用研究,2024,41(6):1743-1748,6.基金项目
智能警务四川省重点实验室课题资助项目(ZNJW2022ZZZD001) （ZNJW2022ZZZD001）
