Financial Decision Knowledge Acquisition Based on Neighborhood Rough Set and Ensemble Classifiers with Grid Search

[Objective] This paper tries to improve the effectiveness and efficiency of acquiring decision-making knowledge from Financial Institutions. [Methods] First, we built a framework of an acquisition system for financial decision-making knowledge, which used neighborhood rough set to remove redundant attributes. Then, we adopted the SMOTE method to balance the data. We also applied grid search method to optimize parameters of the ensemble classifiers. Third, we trained and used the new model to identify the optimal reduction group. Finally, we acquired the needed knowledge through the optimal reduction, and stored them in the database. [Results] We examined the proposed method with 4,521 pieces of financial record, which yielded sensitivity of 83.55%, specificity of 80.74% and AUC of 0.8214. [Limitations] We did not run the proposed model with data of insurance or consumer loans. [Conclusions] The proposed method could improve the classification performance of financial decision-making system, which could identify and acquire knowledge of key customers effectively. © 2023 Chin J Gen Pract. All rights reserved.