Computer Science ›› 2018, Vol. 45 ›› Issue (10): 155-159.doi: 10.11896/j.issn.1002-137X.2018.10.029

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Improved Data Anomaly Detection Method Based on Isolation Forest

XU Dong, WANG Yan-jun, MENG Yu-long, ZHANG Zi-ying   

  1. College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China
  • Received:2017-09-05 Online:2018-11-05 Published:2018-11-05

Abstract: An improved data anomaly detection method namely SA-iForest was proposed to solve the problem of low accuracy,poor execution efficiency and generalization ability of exsiting anomaly data detection algorithm based on isolated forest.The isolation tree with high precision and differences is selected to optimize the forest based on simulated annealing algorithm.At the same time,the redundant isolated trees are removed,and the forest construction of isdated trees is improved.The method of data anomaly detection based on SA-iForest was compared with the traditional Isolation Forest algorithm and LOF algorithm.The accuracy,execution efficiency,and stability of the proposed algorithm have significant improvement through the standard simulation data set.

Key words: Isolation forest, Outlier detection, SA-iForest, Simulated annealing

CLC Number: 

  • TP306
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