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- Title
Abnormal user identification based on XGBoost algorithm.
- Authors
SONG Xiao-yu; SUN Xiang-yang; ZHAO Yang
- Abstract
The eXtreme gradient boosting (XGBoost) algorithm is used to identify abnormal users. Firstly, the raw data were cleaned. Then user power characteristics were extracted from different aspects. Finally, the XGBoost classifier was used to identify the abnormal users respectively in the balanced sample set and the unbalanced sample set. In contrast, under the same characteristics, the K-nearest neighbor (KNN) classifier, back-propagation (HP) neural network classifier and random forest classifier were used to identify the abnormal users in the two samples. The experimental results show that the XGIioost classifier has higher recognition rate and faster running speed. Especially in the imbalanced data sets, the performance improvement is obvious.
- Subjects
DATA extraction; ARTIFICIAL neural networks; BACK propagation
- Publication
Journal of Measurement Science & Instrumentation, 2018, Vol 9, Issue 4, p339
- ISSN
1674-8042
- Publication type
Article
- DOI
10.3969/j.issn.1674-8042.2018.04.006