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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (1): 85-94    DOI: 10.11925/infotech.2096-3467.2018.0323
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Financial Decision Knowledge Acquisition Based on Neighborhood Rough Set and Ensemble Classifiers with Grid Search
Jing Li,Xiao Liu(),Xiaoli Wang
School of Economics and Management, Tongji University, Shanghai 200092, China
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[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.

Key wordsKnowledge Acquisition      Neighborhood Rough Set      Ensemble Classifiers      Grid Search     
Received: 25 March 2018      Published: 04 March 2019

Cite this article:

Jing Li,Xiao Liu,Xiaoli Wang. Financial Decision Knowledge Acquisition Based on Neighborhood Rough Set and Ensemble Classifiers with Grid Search. Data Analysis and Knowledge Discovery, 2019, 3(1): 85-94.

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