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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