Abstract
As the digital world grows, data is being collected at high speed on a continuous and real-time scale. Hence, the imposed imbalanced and evolving scenario that introduces learning from streaming data remains a challenge. As the research field is still open to consistent strategies that assess continuous and evolving data properties, this paper proposes an unsupervised, online, and incremental anomaly detection ensemble of influence trees that implement adaptive mechanisms to deal with inactive or saturated leaves. This proposal features the fourth standardized moment, also known as kurtosis, as the splitting criteria and the isolation score, Shannon’s information content, and the influence function of an instance as the anomaly score. In addition to improving interpretability, this proposal is also evaluated on publicly available datasets, providing a detailed discussion of the results.
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Acknowledgements
This work has been supported by Fundação para a Ciência e Tecnologia (FCT), Portugal - 2021.04908.BD, NOVA LINCS - UIDB/04516/2020, CityCatalyst - POCI-01-0247-FEDER-046119, financed by FEDER, and by the CHIST-ERA grant CHIST-ERA-19-XAI-012, and project CHIST-ERA/0004/2019 and partially supported by the CHIST-ERA grant CHIST-ERA-19-XAI-012, funded by FCT. Also, this work is financed by the ERDF - European Regional Development Fund, through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme under the Portugal 2020 Partnership Agreement, within project City Analyser, with reference POCI-01-0247-FEDER-039924.
All the supports mentioned above are gratefully acknowledged.
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Martins, I., Resende, J.S., Gama, J. (2023). Online Influence Forest for Streaming Anomaly Detection. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_22
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