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Contextual Word Embeddings Clustering Through Multiway Analysis: A Comparative Study Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Online Influence Forest for Streaming Anomaly Detection

Authors : Inês Martins, João S. Resende, João Gama

Published in: Advances in Intelligent Data Analysis XXI

Publisher: Springer Nature Switzerland

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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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Metadata
Title
Online Influence Forest for Streaming Anomaly Detection
Authors
Inês Martins
João S. Resende
João Gama
Copyright Year
2023
Book
Advances in Intelligent Data Analysis XXI

Print ISBN: 978-3-031-30046-2

Electronic ISBN: 978-3-031-30047-9

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-30047-9_22

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