Stress-Testing Hoeffding Trees

Holmes, Geoff; Kirkby, Richard; Pfahringer, Bernhard

doi:10.1007/11564126_50

Cited by 44 publications

(22 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have tested the three versions of Hoeffding Adaptive Tree, HAT-INC, HAT-EWMA(α = .01), HAT-ADWIN, each with and without the addition of Naïve Bayes (NB) classifiers at the leaves. As a general comment on the results, the use of NB classifiers does not always improve the results, although it does make a good difference in some cases; this was observed in [HKP05], where a more detailed analysis can be found.…”

Section: Experimental Evaluationmentioning

confidence: 88%

Adaptive learning and mining for data streams and frequent patterns

Bifet

2009

SIGKDD Explor. Newsl.

View full text Add to dashboard Cite

ADVERTIMENT. La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del servei TDX (www.tesisenxarxa.net) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tesisenred.net) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING.On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the TDX (www.tesisenxarxa.net) service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized neither its spreading and availability from a site foreign to the TDX service. Introducing its content in a window or frame foreign to the TDX service is not authorized (framing). This rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it's obliged to indicate the name of the author AbstractThis thesis is devoted to the design of data mining algorithms for evolving data streams and for the extraction of closed frequent trees. First, we deal with each of these tasks separately, and then we deal with them together, developing classification methods for data streams containing items that are trees.In the data stream model, data arrive at high speed, and the algorithms that must process them have very strict constraints of space and time. In the first part of this thesis we propose and illustrate a framework for developing algorithms that can adaptively learn from data streams that change over time. Our methods are based on using change detectors and estimator modules at the right places. We propose an adaptive sliding window algorithm ADWIN for detecting change and keeping updated statistics from a data ...

show abstract

Section: Experimental Evaluationmentioning

confidence: 88%

Adaptive learning and mining for data streams and frequent patterns

Bifet

2009

SIGKDD Explor. Newsl.

View full text Add to dashboard Cite

show abstract

“…The authors also cite work by Holmes et al [19] about findings regarding the use of Naïve Bayes models at the leaves of the Hoeffding Trees and its advantages as well as disadvantages. The authors reiterate their thoughts on the excellent predictive performance for evolving data streams through the use of bagging using ADWIN [16] based on the online bagging method of [20] with the ADWIN algorithm for detecting accuracy changes for each ensemble members.…”

Section: Ensembles Of Restricted Hoeffding Trees Using Stackingmentioning

confidence: 99%

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

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.

show abstract

“…There are exceptional cases shown in [143], where a standard Hoeffding Option Tree will outperform the tree with functional nodes. The author of [143] proposes an adaptive approach, where the training algorithm adaptively decides to use the functional or majority votes, based on the current performance of each of them. This implementation is adopted in our framework to enable efficient online learning.…”

Section: Hoeffding Option Trees With Functional Leavesmentioning

confidence: 99%

Computational Inference and Control of Quality in Multimedia Services

Menkovski

2015

Springer Theses

View full text Add to dashboard Cite

Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:www.tue.nl/taverne Take down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nl providing details and we will investigate your claim. Copyright c 2013 by Vlado Menkovski All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior written consent of the author. Typeset using L A T E X, printed in The NetherlandsTo my parents, Angel who dedicated his life to his sons and Snežana whose strength inspires me deeply. SummaryQuality is the degree of excellence we expect of a service or a product. It is also one of the key factors that determine its value. For multimedia services, understanding the experienced quality means understanding how the delivered fidelity, precision and reliability correspond to the users' expectations. Yet the quality of multimedia services is inextricably linked to the underlying technology. It is developments in video recording, compression and transport as well as display technologies that enables high quality multimedia services to become ubiquitous. The constant evolution of these technologies delivers a steady increase in performance, but also a growing level of complexity. As new technologies stack on top of each other the interactions between them and their components become more intricate and obscure. In this environment optimizing the delivered quality of multimedia services becomes increasingly challenging. The factors that affect the experienced quality, or Quality of Experience (QoE), tend to have complex non-linear relat...

show abstract

Stress-Testing Hoeffding Trees

Cited by 44 publications

References 3 publications

Adaptive learning and mining for data streams and frequent patterns

Adaptive learning and mining for data streams and frequent patterns

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

Computational Inference and Control of Quality in Multimedia Services

Contact Info

Product

Resources

About