An FCM clustering algorithm based on the identification of accounting statement whitewashing behavior in universities

The traditional recognition method of whitewash behavior of accounting statements needs to analyze a large number of special data samples. The learning rate of the algorithm is low, resulting in low recognition accuracy. To solve the aforementioned problems, this article proposes a method to identif...

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Vydáno v:Journal of intelligent systems Ročník 31; číslo 1; s. 345 - 355
Hlavní autor: Yang, Qihao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin De Gruyter 05.03.2022
Walter de Gruyter GmbH
Témata:
Algorithms
behavior recognition
Clustering
FCM clustering algorithm
Fuzzy sets
Identification methods
Machine learning
Motivation
Recognition
university accounting statements
whitewashing behavior
ISSN:2191-026X, 0334-1860, 2191-026X
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Shrnutí:The traditional recognition method of whitewash behavior of accounting statements needs to analyze a large number of special data samples. The learning rate of the algorithm is low, resulting in low recognition accuracy. To solve the aforementioned problems, this article proposes a method to identify the whitewash behavior of university accounting statements based on the FCM clustering algorithm. This article analyzes the motivation of university accounting statement whitewashing behavior, studies the common means of statement whitewashing, and establishes a fuzzy set for the identification of university accounting statement whitewashing behavior. By calculating the fuzzy partition coefficient, the membership matrix of whitewash behavior recognition is established, and the whitewash behavior is classified through the iteration of the FCM algorithm. The comparative experimental results show that the recognition method has good recognition performance, low recognition error rate, and recognition accuracy of 82%.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2191-026X
0334-1860
2191-026X
DOI:10.1515/jisys-2022-0022