[Objective] A new model based on Principle Component Analysis and Random Forest is proposed aiming to detect Taobao brushers, decrease the dimensions of indicators and improve recognition rate. [Methods] This article uses Principle Component Analysis to reduce dimensions and uses Random Forest to classify users. In order to reflect the superiority of the detection model, it also builds detection models respectively based on KNN and SVM using the same data for different model training to compare the detection accuracy and efficiency of these models. [Results] The experimental results show that the detection model on the Principle Component Analysis and Random Forest gets 88.0% accuracy within 3 minutes. [Limitations] Most data is from third-party platforms which cannot fully reflect the all Singlebrush types. [Conclusions] The detection model on the Principle Component Analysis and Random Forest has higher detection accuracy and efficiency.
张李义, 张皎. 一种基于主成分分析和随机森林的刷客识别方法[J]. 现代图书情报技术, 2015, 31(10): 65-71.
Zhang Liyi, Zhang Jiao. A Brusher Detection Method Based on Principle Component Analysis and Random Forest. New Technology of Library and Information Service, 2015, 31(10): 65-71.
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