Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (8): 185-188.doi: 10.11896/jsjkx.190600162

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FS-CRF:Outlier Detection Model Based on Feature Segmentation and Cascaded Random Forest

LIU Zhen-peng1, 2, SU Nan1, QIN Yi-wen3, LU Jia-huan1, LI Xiao-fei2   

  1. 1 School of Cyber Security and Computer, Hebei University, Baoding, Hebei 071002, China
    2 Information Technology Center, Hebei University, Baoding, Hebei 071002, China
    3 School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:LIU Zhen-peng, born in 1966, Ph.D, professor, is a senior member of China Computer Federation.His main research inte-rests include network information security and outlier detection.
    LI Xiao-fei, born in 1979, master, engineer.Her main research interests include network information security and outlier detection.
  • Supported by:
    This work was supported by the Natural Science Foundation of Hebei Province, China (F2019201427) andMinistry of Education Fund for “Integration of Cloud Computing and Big Data, Innovation of Science and Education”, China (2017A20004).

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Abstract: In the era of big data, there are many abnormal values hidden in massive data due to attack tampering, equipment fai-lure, artificial fraud and other reasons.Accurately detect outliers in data is critical to data cleaning.Therefore, an outlier detection model combining feature segmentation and multi-level cascaded random forest (FS-CRF) is proposed.Using the sliding window and the random forest to segment the original features, the generated class probability vector is used to train the multi-level cascaded random forest.Finally, the category of the sample is determined by the vote of the last layer.Simulation experiment results show that the new method can effectively detect outlier in classification tasks on UCI data sets, with high F1-measure scores obtained on both high and low dimensional data sets.The cascade structure further improves the generalization ability of the model compared to the classical random forest.Compared with the GBDT and XGBoost, the proposed method has performance advantages on high-dimensional data sets, and has fewer hyper-parameters that easy to tune and has better comprehensive performance.

Key words: Cascade random forest, Data cleaning, Ensemble learning, Grained feature, Outlier detection

CLC Number: 

  • TP301
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