Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation

• URL: http://arxiv.org/abs/2305.08977v2
• Date: Tue, 5 Sep 2023 18:31:18 GMT
• Title: Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation
• Authors: Jin Li, Kleanthis Malialis, Marios M. Polycarpou
• Abstract summary: The paper proposes an autoencoder-based incremental learning method with drift detection (strAEm++DD) Our proposed method strAEm++DD leverages on the advantages of both incremental learning and drift detection. We conduct an experimental study using real-world and synthetic datasets with severe or extreme class imbalance, and provide an empirical analysis of strAEm++DD.
• Score: 10.41066461952124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas. These data are often unlabelled. In this case, identifying infrequent events, such as anomalies, poses a great challenge. This problem becomes even more difficult in non-stationary environments, which can cause deterioration of the predictive performance of a model. To address the above challenges, the paper proposes an autoencoder-based incremental learning method with drift detection (strAEm++DD). Our proposed method strAEm++DD leverages on the advantages of both incremental learning and drift detection. We conduct an experimental study using real-world and synthetic datasets with severe or extreme class imbalance, and provide an empirical analysis of strAEm++DD. We further conduct a comparative study, showing that the proposed method significantly outperforms existing baseline and advanced methods.

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