Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences
<|reference_start|>Unsupervised Incremental Learning with Dual Concept Drift Detection for Identifying Anomalous Sequences: In the contemporary digital landscape, the continuous generation of extensive streaming data across diverse domains has become pervasive. Yet, a significant portion of this data remains unlabeled, posing a challenge in identifying infrequent events such as anomalies. This challenge is further amplified in non-stationary environments, where the performance of models can degrade over time due to concept drift. To address these challenges, this paper introduces a new method referred to as VAE4AS (Variational Autoencoder for Anomalous Sequences). VAE4AS integrates incremental learning with dual drift detection mechanisms, employing both a statistical test and a distance-based test. The anomaly detection is facilitated by a Variational Autoencoder. To gauge the effectiveness of VAE4AS, a comprehensive experimental study is conducted using real-world and synthetic datasets characterized by anomalous rates below 10\% and recurrent drift. The results show that the proposed method surpasses both robust baselines and state-of-the-art techniques, providing compelling evidence for their efficacy in effectively addressing some of the challenges associated with anomalous sequence detection in non-stationary streaming data.<|reference_end|>
@article{li2024unsupervised,
    title={Unsupervised Incremental Learning with Dual Concept Drift Detection for
  Identifying Anomalous Sequences},
    author={Jin Li, Kleanthis Malialis, Christos G. Panayiotou, Marios M.
  Polycarpou},
    journal={arXiv preprint arXiv:2403.03576},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03576},
    primaryClass={cs.CE}
}