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- W4385482647 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 incremen-tal 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 outper-forms existing baseline and advanced methods." @default.
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- W4385482647 date "2023-06-18" @default.
- W4385482647 modified "2023-09-30" @default.
- W4385482647 title "Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation" @default.
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- W4385482647 doi "https://doi.org/10.1109/ijcnn54540.2023.10191328" @default.
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