| Abstrakt: |
Aiming at the large identification error and long identification time in online consumer behaviour anomaly identification, an online consumer behaviour anomaly identification method based on limit learning machine is designed. The key factors affecting the characteristics of consumers' online consumption behaviour are determined, and the data characteristics are extracted by using classical TRA theory and decision tree. The similar feature data are determined by non-negative matrix decomposition method; the fused feature data are placed in two-dimensional space, and the noise points in the feature data are located by gradient matrix algorithm under Gaussian window. Determine the state of characteristic data, train the suspected abnormal behaviour data through the limit learning machine, randomly add weights and bias values in the training, output the results, and modify the results through the correction function to complete the anomaly identification. The results show that the accuracy error of the proposed method is about 0.8%. |
