Abstract: The current problem of unbalanced distribution of positive and negative training samples has greatly limited the performance of outlier detection models.The outlier detection algorithm based on active learning can automatically synthesize outliers to balance the training data through active learning of sample distribution.However,the traditional detection method based on active learning lacks the quality assessment and filtering of synthetic outliers,which leads to the fact that the noise in the synthetic training samples degrades the performance of classification models.Aiming at the above problems,a multi-generator adversarial learning algorithm based on reverse label propagation(MG-RLP) is proposed,which consists of multiple neural network generators and a discriminator for outlier boundary detection.MG-RLP uses multiple sub-generators to generate sample data with multi-distribution features to prevent the mode collapse problem caused by the excessive aggregation of training samples synthesized by a single generator.At the same time,the proposed method utilizes the reverse label propagation to evaluate the quality of the sample points generated to screen out credible synthetic samples.The filtered samples are retained in the training samples to iteratively train the discriminator to improve the detection performance of outliers.The MG-RLP is compared with six typical outlier detection algorithms on five public datasets.The results show that the proposed algorithm improves AUC and detection precision by 15% and 22% respectively,which verifies its effectiveness.

Key words: Outlier detection, Active learning, Generative adversarial networks, Label propagation