基于集成学习的三支决策模型

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中图分类号：TP18 文献标志码：A DOI：10.13705/j. issn.1671-6841.2024095

文章编号：1671-6841（2025）06-0042-09

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 efectiveness in handling complicated decision problems.To enhance the robustnessand accuracy of the decision-making process，a novel three-way decision model based on ensemble learning was proposed.Firstly，diffrent decision criteria were adopted in the decision-making process to obtain diferent 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 effcacy of this newly proposed model were substantiated through extensive experiments on diffrent 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.

Key words： three-way decision; ensemble learning； cluster ensemble; cluster analysis

0 引言

随着数据规模的不断增加，当今决策者面临着更加复杂的决策环境。（剩余11142字）