基于随机投影与集成学习的离群点检测算法.

To address the problem that traditional similarity-based outlier detection algorithms were not effective enough on high-dimensional unbalanced datasets, this paper proposed a novel Ensemble learning and Random projection-based Outlier Detection (EROD） framework. Firstly, the EROD algorithm integrated several random projection methods to reduce the dimensionality of high-dimensional data, which improved the data diversity. Secondly, it integrated several different traditional outlier detectors to build a heterogeneous ensemble model, which increased the robustness of the algorithm. Finally, the EROD acquired the final outlier value of the object by using the heterogeneous ensemble model to train the reduced dimensional data and by using two optimal combinations of the trained model to reduce the total error, and the algorithm determined the object with high outlier value as outlier point. The results showed that the algorithm had an average improvement of 3.6% and 14.45% in AUC and Precision @ n value compared with the traditional outlier detection algorithm and the outlier detection algorithm based on ensemble learning. Therefore, the EROD algorithm has the advantage of handling the anomalies of high-dimensional unbalanced data. [ABSTRACT FROM AUTHOR]