A three-way risk rating method integrating expert domain knowledge and K-means clustering
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.
国家科技期刊平台
NSTL
万方数据
维普
