Locality and Consistency Based Sequential Ensemble Method for Outlier Detection

Anomaly detection has been widely used in many application fields, such as network intrusion detection, credit card fraud detection, etc. The increase of data dimension leads to the emergence of many irrelevant and redundant features, which can mask relevant features and produce false positive results. Dimensional data has sparsity and distance clustering effects, and traditional anomaly detection algorithms based on density and distance are no longer applicable. Most of the anomaly detection research based on machine learning focuses on a single model, and a single model has the ability to resist overfitting. There are certain shortcomings. The ensemble learning model has good generalization ability, and shows better prediction accuracy than a single model in practical applications. In this paper, an anomaly detection sequence ensemble method based on neighborhood consistency (Locality and Consistency Based Sequential Ensemble Method for Outlier Detection, LCSE). Firstly, the basic model of anomaly detection