### Outlier detection algorithm based on autoencoder and ensemble learning

• Received:2021-05-10 Revised:2021-09-08 Online:2021-09-15 Published:2021-09-15

### 基于自编码器与集成学习的离群点检测算法

1. 1.新疆大学 信息科学与工程学院，乌鲁木齐 830046
2.新疆大学 软件学院，乌鲁木齐 830091
3.中国海洋大学 信息科学与工程学院，山东 青岛 266100
• 通讯作者: 于炯

Abstract: The outlier detection algorithm based on self encoder is easy to fit on small and medium-sized data sets, and the traditional outlier detection algorithm based on ensemble learning does not optimize the base detector, resulting in low detection accuracy. Aiming at the problems above, an Ensemble and Autoencoder-based Outlier Detection (EAOD) algorithm which applied autoencoder into ensemble learning was proposed. Firstly, in order that outlier values and outlier degree marker values for data objects were obtained, the connection structure of the autoencoder was randomly changed to generate different base detectors. Secondly, local region around the object was constructed according to the Euclidean distance calculated by the nearest neighbour algorithm. Finally, based on the similarity between the outlier and the outlier degree marker value, base detectors with strong detection ability were selected and combined in the region, and the outlier of the combined object was used as the final outlier of EAOD. Compared with the AutoEncoder(AE) algorithm, the AUC and AP values of the proposed algorithm were increased by 8.08 and 9.17 percentage points respectively on the Cardio dataset; compared with the Feature Bagging(FB) ensemble learning algorithm, the running time cost was reduced by 21.33% on the Mnist dataset. Above experimental results show that the algorithm has good detection performance and real-time performance under unsupervised learning.

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