关键词: 职业举报人, 类不平衡, 特征选择, 数据驱动, 12358价格监管平台

Abstract: “Professional whistleblower” is a problem that has perplexed market regulators for many years, and with the trend of gangs, large-scale, professional and low-age. Most of the government departments take the manual identification methods to identify “professional whistleblower”, which uses up much labor power. This paper uses the statistical technique “bootstrapping”, combined with the characteristics of text, time and whistleblower attributes, on the basis of solving the problem of over-fitting of class unbalanced data, the accurate identification of “professional whistleblower” is realized. The experimental results show that：the recognition accuracy of “bootstrapping” is higher than that of other resampling methods such as “oversampling” and “undersampling”, the correlation-based feature selection method combined with the best first search strategy to optimize the data features in the identification method has higher computational efficiency on the premise of ensuring the accuracy. By the real-world data-driven of “national 12358 price regulation platform”, this paper verifies the effectiveness of the method. Finally, this paper compares and analyzes the differences of the behaviors between professional whistleblower and normal consumers.

Key words: professional whistleblower, class imbalance, feature selection, data driven, 12358 price regulation platform