ADMSFI : Anomaly Detection Based on Multi-Sequence Fuzzy Feature Interaction

As a core problem in unsupervised learning, anomaly detection focuses on identifying abnormal patterns in datasets, thereby providing support for uncovering potential problems and extracting valuable information. However, most existing methods fail to extract sufficient information in feature interactions when dealing with heterogeneous datasets. To address this challenge, a novel anomaly detection method based on multi-sequence fuzzy feature interaction is proposed. Firstly, we propose multi-sequence features based on joint fuzzy information entropy to capture complex feature interactions and to quantify the interdependencies among features. Secondly, forward and reverse multi-sequence feature subset pairs are constructed to characterize the correlation between features from different angles, enhancing the accuracy of representing complex interactions in heterogeneous data and improving the ability to identify potential anomalies. Subsequently, an uncertainty measure based on multi-sequence information fusion is introduced, and anomaly scores are accumulated by incorporating instance weights, thereby ensuring stable detection performance in heterogeneous datasets. Finally, an anomaly detection algorithm based on multi-sequence fuzzy feature interaction (ADMSFI) is proposed. The experimental results demonstrate that the proposed algorithm ADMSFI significantly outperforms 13 existing algorithms in terms of performance and flexibility in 24 datasets.