A Survey on Ensemble Learning for Data Stream Classification

Gomes, Heitor Murilo; Barddal, Jean Paul; Enembreck, Fabrício; Bifet, Albert

doi:10.1145/3054925

Cited by 411 publications

(202 citation statements)

References 118 publications

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“…Due For the proposed taxonomy in [26], the authors not only arrange ensemble-related techniques based on diversity, base learner, and combination, but they also discuss characteristics that influence the ensemble formulation that are unique to data stream learning in which they refer to as "update dynamics". An To review the ensemble algorithms for data stream learning from a broader perspective, the authors in [26], discuss the heuristics in the development of new ensemble algorithms, the computational resources management and big data perform experiments using these frameworks.…”

Section: Literature Review On Ensemblesmentioning

confidence: 99%

“…An To review the ensemble algorithms for data stream learning from a broader perspective, the authors in [26], discuss the heuristics in the development of new ensemble algorithms, the computational resources management and big data perform experiments using these frameworks.…”

Section: Literature Review On Ensemblesmentioning

confidence: 99%

“…The authors in [26] conclude their work as a presentation to the main characteristics of ensemble learners, identifying open-source software, and discussing current and expected future trends in ensemble learning for data streams, such as concept and feature evolution, diversity measurement, big data stream learning, temporal dependencies, and instance labeling.…”

Section: Literature Review On Ensemblesmentioning

confidence: 99%

“…Our survey differentiates from [26], as we focus on ensembles of Hoeffding Trees in the context of anomaly detection in streaming cyber datasets. The information presented from the authors in [26] can be used as a basis for understanding our work and the need for this type of research on ensemble learning in reference to real-world problems and/or data stream classification in general.…”

Section: Literature Review On Ensemblesmentioning

confidence: 99%

“…The information presented from the authors in [26] can be used as a basis for understanding our work and the need for this type of research on ensemble learning in reference to real-world problems and/or data stream classification in general.…”

Section: Literature Review On Ensemblesmentioning

confidence: 99%

See 4 more Smart Citations

Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

Muallem¹,

Shetty²,

Pan³

et al. 2017

JIS

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This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions within this paper and were selected based on their relation to streaming data and the flexibility of their techniques for use within different domains of streaming data. We discuss the relevance of how combining the techniques of the proposed research works within these compositions can be used to address the anomaly detection problem in streaming cyber datasets. The goal is to show how a combination of techniques from different compositions can solve a prominent problem, anomaly detection.

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Section: Literature Review On Ensemblesmentioning

confidence: 99%

Section: Literature Review On Ensemblesmentioning

confidence: 99%

Section: Literature Review On Ensemblesmentioning

confidence: 99%

Section: Literature Review On Ensemblesmentioning

confidence: 99%

Section: Literature Review On Ensemblesmentioning

confidence: 99%

See 3 more Smart Citations

Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

Muallem¹,

Shetty²,

Pan³

et al. 2017

JIS

View full text Add to dashboard Cite

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AE‐Integrated: Real‐time network intrusion detection with Apache Kafka and autoencoder

Roshan,

Zafar

2024

Concurrency and Computation

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SummaryUnknown cyber‐attack detection in network traffic streams is challenging but crucial to ensure network security. It is observed that new security threats occur on a daily basis and make cyberspace vulnerable. In the literature, machine learning and deep learning‐based network intrusion detection systems have gained a lot of success but still face many challenges in detecting new security threats and unknown cyber‐attacks in real‐time. Additionally, high false alarm rates and real‐time detection in constantly evolving high‐dimensional network data streams are open issues for the research community. To address this issue, a DL‐based solution is developed to detect real‐time network anomalies in streaming data with high detection accuracy, precision, recall and low false negative and positive scenarios. The proposed novel algorithm, AE‐Integrated, is developed and evaluated on the latest CICIDS‐2017 dataset. The AE‐Integrated is updated with the newest network traffic data stream by the human administrator after a certain period to maintain its prediction accuracy for future inference. The simulation study is conducted with the Apache Kafka and Slack API to get real‐time anomaly alerts. Finally, we compared the result with recent state‐of‐the‐art research to evaluate the significance of the proposed algorithm. It is concluded that combining multiple lightweight autoencoders into a single large architecture provides optimal results. The accuracy, recall, and AUC of AE‐Integrated obtained are 99.54%, 99.53%, and 0.998, respectively.

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Adaptiveness and consistency of a class of online ensemble learning algorithms

Young

Yao

Zhang

2020

Intl J Robust & Nonlinear

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Summary Expert based ensemble learning algorithms often serve as online learning algorithms for an unknown, possibly time‐varying, probability distribution. Their simplicity allows flexibility in design choices, leading to variations that balance adaptiveness and consistency. This article provides an analytical framework to quantify the adaptiveness and consistency of expert based ensemble learning algorithms. With properly selected states, the algorithms are modeled as a Markov chains. Then quantitative metrics of adaptiveness and consistency can be calculated through mathematical formulas, other than relying on numerical simulations. Results are derived for several popular ensemble learning algorithms. Success of the method has also been demonstrated in both simulation and experimental results.

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A Survey on Ensemble Learning for Data Stream Classification

Cited by 411 publications

References 118 publications

Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

AE‐Integrated: Real‐time network intrusion detection with Apache Kafka and autoencoder

Adaptiveness and consistency of a class of online ensemble learning algorithms

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