Abstract: Outlier detection is an important data mining method, which is used to preprocess data and mine heterogeneous data information. In recent years, due to the problem of dimension disaster, it is very difficult to detect the high-dimensional outlier data. Aiming at the above problems, a semi- supervised outlier detection model based on autoencoder and integrated learning is proposed. Firstly, autoencoder is used to reduce the dimension and increase the outlier degree of the outlier data. Secondly, considering that Iforest, lof and k-means algorithms are sensitive to different outlier types, they are fused in the AdaBoost boosting framework to improve the accuracy of outlier detection. The results show that, compared with the current mainstream outlier detection methods, the proposal significantly improves the accuracy of the model.

Key words: outlier detection, boosting framework, semi-supervised；autoencoder