陕西师范大学学报（自然科学版）2024，Vol.52Issue(3)：26-36,11.DOI:10.15983/j.cnki.jsnu.2024007
融合专家领域知识和K-means聚类的三支风险评级方法
A three-way risk rating method integrating expert domain knowledge and K-means clustering
摘要
Abstract
In practical domains such as finance and healthcare,decision-making problems necessitate through the consideration of risks,where precise prediction and accurate risk classification hold crucial significance.Nevertheless,traditional group decision-making studies prioritize the consistency and consensus of expert evaluations while allocating lesser attention to acquiring objective evaluations and the decision quality.Consequently,a data-driven approach is introduced to assist experts in discovering evaluation through data and clustering results,optimizing group opinions within the three-way decision framework so as to improve and calculate the discriminative point of logistic regression for the results of risk rating classification.The risk rating is determined based on four publicly available datasets of credit risk and disease diagnosis from UCI and Kaggle.Empirical results from data experiments indicate that our proposed three-way classification method focuses more on risk avoidance compared to classical machine learning methods,and achieves stable and superior performance across all datasets.This implies that utilizing objective information from data to assist expert evaluations in risk assessment can help to solve decision problems within different domains.关键词
专家领域知识/聚类分析/风险评级/三支决策/决策质量Key words
expert domain knowledge/clustering analysis/risk rating/three-way decision/decision quality分类
数理科学引用本文复制引用
段维怡,梁德翠..融合专家领域知识和K-means聚类的三支风险评级方法[J].陕西师范大学学报（自然科学版）,2024,52(3):26-36,11.基金项目
国家自然科学基金(72071030) （72071030）
教育部人文社会科学规划基金(19YJA630042) （19YJA630042）
