It is difficult to track and identify shill bidding behavior brought by the development of online auctions. In order to solve this problem, this paper used the XGBoost algorithm to build a shill bidding pre-warning model and obtained the important characteristics of identifying this behavior. By comparing with other mainstream algorithms, it is found that the XGBoost algorithm has the highest accuracy in predicting the risk of shill bidding behavior, reaching 99.6%. Through the experiment and comparison of the number of features, three key indicators for identifying shill bidding behavior are found, which provides an accurate range for the attack and prevention. The research in this paper improves the identification ability of shill bidding behavior and reduces the scope of the identification characteristics of shill bidding behavior, which will effectively curb the continuous spread of shill bidding behavior.
In recent years, the fraud crime rate in China is rising, which has seriously endangered the personal and property safety of citizens. Crime is the result of the comprehensive action of society, economy, politics and culture. Based on the data of urbanization and fraud rate in J Province from 2005 to 2019, through Pearson correlation analysis, it is found that urbanization has a significant impact on fraud crime rate. The artificial neural network algorithm is used to predict the crime rate of fraud, with the accuracy rate 93.7%.
Currently, for the problem of personal credit risk identification, the most commonly used method is to optimize the parameters of the model through bionic algorithms to obtain higher accuracy, but it may face the risk of lower precision. Some scholars also discussed the identification of personal credit risk from the perspective of combination models. From the perspective of integrated learning, based on C5.0 algorithm and using boosting technology, this paper constructs the boosting-c5.0 personal credit risk identification model, and uses UCI German personal credit data set to verify the performance of the model. The study found that the accuracy, recall, precision and AUC value of boosting-C5.0 model are better than SVM, logistic and C5.0 models.
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