面向简化规则的集成学习模型及规则约简策略.

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. [ABSTRACT FROM AUTHOR]