Research on credit assessment model based on information entropy 
and LSSVM

Junhong Guo

doi:10.1117/12.2671469

16 March 2023 Research on credit assessment model based on information entropy and LSSVM

Junhong Guo

Author Affiliations +

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 1259303 (2023) https://doi.org/10.1117/12.2671469
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

Credit assessment models are an important basis for financial credit institutions to determine whether to lend or not, so an efficient and accurate credit assessment model is crucial for financial credit institutions. Traditional credit assessment algorithms do not take into account the noise problem caused by the massive amount of credit data, which greatly affects the time complexity and accuracy of credit assessment algorithms. In view of this, this paper proposes a credit assessment method based on information entropy and LSSVM. The method first uses information entropy to assign weights to feature attributes, then sets thresholds on them for feature extraction, and constructs an LSSVM model to evaluate credit data, so as to achieve accurate assessment of credit transaction risk. The experimental results show that the method can effectively reduce the time complexity of the algorithm and improve the accuracy of prediction

Citation Download Citation

Junhong Guo "Research on credit assessment model based on information entropy and LSSVM", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 1259303 (16 March 2023); https://doi.org/10.1117/12.2671469

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