Multi-label Learning with User Credit Data in China Based on MLKNN

Zhang, Zhuangyi; Han, Lu; Chen, Muzi

doi:10.1145/3548636.3548652

Cited by 2 publications

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“…The basic idea of the traditional MLKNN algorithm is to draw on the idea of the KNN algorithm to find K samples adjacent to the predicted sample, count the number of each label in the K samples, and then calculate the probability of the test sample containing each label through the maximum posterior probability [45]. The label whose predicted probability is greater than a certain threshold is the label of the predicted sample.…”

Section: Fuzzy Mlknnmentioning

confidence: 99%

Fuzzy MLKNN in Credit User Portrait

2022

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Aiming at the problems of subjective enhancement caused by the discretization of credit data and the lack of a multi-dimensional portrait of credit users in the current credit data research, this paper proposes an improved Fuzzy MLKNN multi-label learning algorithm based on MLKNN. On the one hand, the subjectivity of credit data after discretization is weakened by introducing intuitionistic fuzzy numbers. On the other hand, the algorithm is improved by using the corresponding fuzzy Euclidean distance to realize the multi-label portrait of credit users. The experimental results show that Fuzzy MLKNN performs significantly better than MLKNN on credit data and has the most significant improvement on One Error.

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Section: Fuzzy Mlknnmentioning

confidence: 99%

Fuzzy MLKNN in Credit User Portrait

2022

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“…Conventional mainstream user profiling methods encompass collaborative filtering, content-based, and knowledge-based approaches [24]. In the domain of credit risk, the utilization of user profiling is primarily centered on machine learning methods with black-box attributes [25][26][27][28]. However, the current research is limited to the credit rating level.…”

Section: Introductionmentioning

confidence: 99%

Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk

Zhu,

Shia,

et al. 2024

Mathematics

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Online consumer credit services play a vital role in the contemporary consumer market. To foster their sustainable development, it is essential to establish and strengthen the relevant risk management mechanism. This study proposes an intelligent management framework called the consumer default risk portrait (CDRP) to mitigate the default risks associated with online consumer loans. The CDRP framework combines traditional credit information and Internet platform data to depict the portrait of consumer default risks. It consists of four modules: addressing data imbalances, establishing relationships between user characteristics and the default risk, analyzing the influence of different variables on default, and ultimately presenting personalized consumer profiles. Empirical findings reveal that “Repayment Periods”, “Loan Amount”, and “Debt to Income Type” emerge as the three variables with the most significant impact on default. “Re-payment Periods” and “Debt to Income Type” demonstrate a positive correlation with default probability, while a lower “Loan Amount” corresponds to a higher likelihood of default. Additionally, our verification highlights that the significance of variables varies across different samples, thereby presenting a personalized portrait from a single sample. In conclusion, the proposed framework provides valuable suggestions and insights for financial institutions and Internet platform managers to improve the market environment of online consumer credit services.

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Multi-label Learning with User Credit Data in China Based on MLKNN

Cited by 2 publications

References 0 publications

Fuzzy MLKNN in Credit User Portrait

Fuzzy MLKNN in Credit User Portrait

Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk

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