RETRACTED ARTICLE: Data mining optimization model for financial management information system based on improved genetic algorithm

Wei Li (), Qiling Zhou, Junying Ren and Samantha Spector
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Wei Li: Dalian Maritime University
Qiling Zhou: Dalian Maritime University
Junying Ren: Dalian Maritime University
Samantha Spector: State University of New York at Albany

Information Systems and e-Business Management, 2020, vol. 18, issue 4, No 17, 747-765

Abstract: Abstract The traditional corporate financial diagnosis method is susceptible to the choice of accounting policies, and there are serious lags, one-sidedness and limitations. A financial management information system based on improved genetic algorithm is proposed based on the financial management information system data mining and clustering analysis model framework, and based on the financial analysis related knowledge. By adopting the event-driven architecture, a financial management information system model based on data mining technology is constructed, which not only enables the data warehouse and data mining technology to play a role in decision support, but also enables the financial information and non-financial information of enterprises to be fully utilized. By extracting financial data, using the above decision tree classification algorithm for data mining, classifying tests according to subject categories and business processes, and evaluating the accuracy of the prediction results, and then determining whether the classification algorithm is selected. The test and analysis of the national tax financial analysis system were completed, and three public data sets and three national tax financial expenditure data sets were selected, and the algorithm was tested on the experimental platform. The test results show that the algorithm show good performance for large-scale data sets, especially financial expenditure data sets, and the test accuracy rate is not only stable but also maintains a relatively high range.

Keywords: Genetic algorithm; Financial management system; Data mining; Clustering analysis; Model optimization (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10257-018-00394-4

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