Title: A deep mining method of enterprise financial risk data based on improved support vector machine

Authors: Hui Sun; Li Fang

Addresses: Department of Economics and Management, Jianghai Vocational and Technical College, Yangzhou 225101, China ' Department of Economics and Management, Jianghai Vocational and Technical College, Yangzhou 225101, China

Abstract: Aiming at the problems of low accuracy of enterprise financial risk data mining, large amount of redundant data and low accuracy of data mining in traditional methods, an enterprise financial risk data depth mining method based on improved support vector machine is proposed. Design the deep mining route of financial risk data, fuzzy transform the financial risk data, and complete the preprocessing of enterprise financial risk data. According to the transformation results, by improving the linear separability and nonlinear separability of data in support vector machine, based on the improved support vector machine, the optimal hyperplane in these two cases is obtained to realise the classification of enterprise financial risk data. On this basis, RFID module is designed to complete the in-depth mining of enterprise financial risk data. The experimental results show that the data mining accuracy of the proposed method is 88%.

Keywords: improved support vector machine; financial risk; data mining; linearly separable; linearly inseparable.

DOI: 10.1504/IJICT.2023.134250

International Journal of Information and Communication Technology, 2023 Vol.23 No.3, pp.253 - 265

Received: 11 May 2021
Accepted: 31 Aug 2021
Published online: 15 Oct 2023 *

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