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.

Feature selection is a crucial step for data preprocessing, and it is widely applied in machine learning. It can eliminate features that are redundant or irrelevant from data, thereby improving performance and reducing runtime. The uncertain nature of labels produces unique challenges for high-dimensional data with label ambiguity, which is still an open problem; the structural information of the data is not utilized fully. In this article, we sufficiently consider the structural information of the data, including relevancy between labels and features, redundancy among features, and positive regions, and set up a novel label ambiguity feature selection model via weighted label-fuzzy relevancy and redundancy. Specifically, we first transform the non-label distribution annotations to label distribution annotations by using a label enhancement model. Second, we use a fuzzy similarity relation to quantify how similar samples are in label space. Third, a general label-fuzzy rough set model is created, and then, a novel feature evaluation measure based on weighted label-fuzzy relevancy and redundancy is defined. In this model, general label-fuzzy rough sets are employed to process label ambiguity problems, and the label-fuzzy relevancy and redundancy are weighted with the feature significance with the positive region as the focus. Finally, a feature selection algorithm for label ambiguity that follows the idea of weighted label-fuzzy relevancy and redundancy is proposed. Extensive experiments are conducted on 12 label distribution annotation datasets and 8 multi-label annotation datasets. The results indicate the advantages of our proposed algorithm over state-of-the-art algorithms.