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三支决策是解决复杂决策问题的一种有效方法，但现有的三支决策模型大多基于单个决策标准，可能无法高效地处理决策问题。因此，为解决这一问题，提出了一种基于集成学习的三支决策模型。首先，在决策过程中采用不同的决策标准来获得不同的三支决策结果。之后受悲观多粒度粗糙集思想的启发，利用集合之间的基本操作求解三个决策区域的共识集合。其次，根据对象的相似度，利用k-means算法将不一致集合划分为三个互不相交的子集。最后，分别将这些子集加入各自的共识集合中获得最终的三支决策结果。根据不同数据集上的实验结果可知，所提出的模型与其他传统三支决策模型相比，分类精度和综合评价指标更高，并且有更小的边界区域占比。
Abstract：Three-way decision model is an effective way to deal with complex decision problems by categorizing objects into three distinct decision regions. However, the existing three-way decision models often rely on a single decision criterion, limiting their effectiveness in handling complicated decision problems. To enhance the robustness and accuracy of the decision-making process, a novel three-way decision model based on ensemble learning was proposed. Firstly, different decision criteria were adopted in the decision-making process to obtain different three-way decision results. Then, inspired by the idea of pessimistic multi-granular rough sets, the consensus sets of the three decision regions were acquired by using basic operations between sets, respectively. Next, the k-means algorithm was utilized to divide the objects in the inconsistent set into three disjoint subsets according to their similarities. These subsets were then added to their respective consensus sets to obtain the final three-way decision results. The efficacy of this newly proposed model were substantiated through extensive experiments on different datasets. Based on experimental results across various datasets, the newly proposed model achieved higher classification accuracy and comprehensive evaluation index. Additionally, the new three-way decision model occupied a smaller boundary region compared with other traditional three-way decision models.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2024095
中图分类号:TP181
引用信息:
[1]王迪,钱进,郑明晨.基于集成学习的三支决策模型[J].郑州大学学报(理学版),2025,57(06):42-50.DOI:10.13705/j.issn.1671-6841.2024095.
基金信息:
国家自然科学基金项目(62066014,62466017); 江西省“双千计划”; 江西省自然科学基金项目(20232ACB202013)