Attribute Grouping-based Categorical Outlier Detection Using Isolation Forest Ensemble Strategy

Attribute grouping is one of the effective steps in high-dimensional outlier detection,but the current ensemble strategies in attribute grouping-based outlier detection only take into account the local outlier information within each attribute group,and ignore the global outlier information of all attribute groups,which can lead to a biased ensemble of attribute group outlier information.This paper proposes an attribute grouping outlier detection approach based on Isolated Forest ensemble strategy by using the local and global outlier information of attribute groups.Firstly,attributes are automatically divided into several attribute groups based on the local and global correlation among attributes,and the outlier information of data objects is obtained in each attribute group.Secondly,from the perspective of attribute grouping,the ensemble bias of the current outlier information ensemble strategy is theoretically analyzed,and the ensemble deviation coefficient are defined as the evaluation index of the outlier information ensemble strategy.Then an attribute grouping-based isolation forest ensemble strategy for categorical outlier detection is proposed,this strategy effectively depicts the local and global outlier information of attribute groups and lowers the ensemble bias of attribute group outlier detection.In the end,experimental results on the UCI validate that the ensemble strategy effectively alleviates the ensemble bias and improves the outlier detection performance.Importantly,compared with the competing methods,the algorithm bolsters the AUC index and the detection efficiency by averages of 7.83% and 48.43%.