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

作　　者：张纬之 韩珣 谢志伟[4] 石胜飞[1] Zhang Weizhi;Han Xun;Xie Zhiwei;Shi Shengfei(Faculty of Computing,Harbin Institute of Technology,Harbin 150001,China;Intelligent Policing Key Laboratory of Sichuan Province,Luzhou Sichuan 646000,China;Dept.of Transportation Management,Sichuan Police College,Luzhou Sichuan 646000,China;Heilongjiang Agricultural Reclamation Vocational College,Harbin 150025,China)

机构地区：[1]哈尔滨工业大学计算学部,哈尔滨150001 [2]智能警务四川省重点实验室,四川泸州646000 [3]四川警察学院道路交通管理系,四川泸州646000 [4]黑龙江农垦职业学院,哈尔滨150025

出　　处：《计算机应用研究》2024年第6期1743-1748,共6页Application Research of Computers

基　　金：智能警务四川省重点实验室课题资助项目(ZNJW2022ZZZD001)。

摘　　要：随着机器学习模型的广泛应用,研究者们逐渐认识到这类方法的局限之处。这些模型大多数为黑盒模型,导致其可解释性较差。为了解决这一问题,以集成学习模型为基础,提出了一种基于规则的可解释模型以及规则约简方法,包括生成优化的随机森林模型、冗余规则的发现和约简等步骤。首先,提出了一种随机森林模型的评价方法,并基于强化学习的思想对随机森林模型的关键参数进行了优化,得到了更具可解释性的随机森林模型。其次,对随机森林模型中提取的规则集进行了冗余消除,得到了更加精简的规则集。在公开数据集上的实验结果表明,生成的规则集在预测准确率和可解释性方面均表现优秀。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 paper 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 proposed 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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]