An Improved Data Anomaly Detection Method Based on Isolation Forest

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Abstract

An improved data anomaly detection method SA-iForest is proposed to solve the problem of low accuracy, poor execution efficiency and generalization ability of data anomalies detection algorithm based on isolated forest. Based on the idea of selective integration, the precision and the difference value are taken as the criterion, and the simulated annealing algorithm is used to select the isolation tree with high abnormality detection and differently to optimize the forest. At the same time, the excess detection precision is small and the difference is small Isolation tree improves the forest construction process of isolated forests, which improves the efficiency of the algorithm and improves the efficiency of the algorithm. The method of data anomaly detection based on SA-iForest is compared with the traditional Isolation Forest algorithm and LOF algorithm, and the accuracy and efficiency of the data are verified by the standard simulation data set. There is a significant improvement.

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