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Research on Audit Opinion Prediction of Listed Companies Based on Sparse Principal Component Analysis and Kernel Fuzzy Clustering Algorithm

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成果类型:
期刊论文
作者:
Zeng, Sen;Li, Yanru;Li, Yaqin
通讯作者:
Li, Y.
作者机构:
[Li, Yaqin; Zeng, Sen] Wuhan Polytech Univ, Sch Management, Wuhan 430023, Peoples R China.
[Li, Yanru] Zhongnan Univ Econ & Law, Sch Accounting, Wuhan 430073, Peoples R China.
通讯机构:
School of Management, Wuhan Polytechnic University, Wuhan, China
语种:
英文
期刊:
Mathematical Problems in Engineering
ISSN:
1024-123X
年:
2022
卷:
2022
基金类别:
Philosophy and Social Science research project of Universities in Hubei Province; China University industry research Innovation Fund [2019ITA03044]; Graduate education innovation program of Zhongnan University of Economics and Law [201911135]; Scientific Research Program of Wuhan Polytechnic University [2018J06]
机构署名:
本校为第一机构
院系归属:
管理学院
摘要:
The prediction of audit opinions of listed companies plays a significant role in the security market risk prevention. By introducing machine learning methods, many innovations can be implemented to improve audit quality, lift audit efficiency, and cultivate the keen insight of auditors. However, in a realistic environment, category imbalance and critical feature selection exist in the prediction model of company audit opinions. This paper firstly combines batched sparse principal component analysis (BSPCA) with kernel fuzzy clustering algorithm...

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