Accurate decision trees for mining high-speed data streams

Gama, João; Rocha, Ricardo Couto Antunes da; Medas, Pedro

doi:10.1145/956750.956813

Cited by 217 publications

(97 citation statements)

References 5 publications

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“…These results contradict those reported by Gama et al [3,4], whose conclusion was that Naive Bayes leaves are always better on the LED data. They used a hold out test set and quoted the final accuracy attained.…”

Section: Resultscontrasting

confidence: 92%

Stress-Testing Hoeffding Trees

Holmes

Kirkby

Pfahringer

2005

Knowledge Discovery in Databases: PKDD 2005

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Abstract. Hoeffding trees are state-of-the-art in classification for data streams. They perform prediction by choosing the majority class at each leaf. Their predictive accuracy can be increased by adding Naive Bayes models at the leaves of the trees. By stress-testing these two prediction methods using noise and more complex concepts and an order of magnitude more instances than in previous studies, we discover situations where the Naive Bayes method outperforms the standard Hoeffding tree initially but is eventually overtaken. The reason for this crossover is determined and a hybrid adaptive method is proposed that generally outperforms the two original prediction methods for both simple and complex concepts as well as under noise.

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Section: Resultscontrasting

confidence: 92%

Stress-Testing Hoeffding Trees

Holmes

Kirkby

Pfahringer

2005

Knowledge Discovery in Databases: PKDD 2005

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“…The decision tree classifier has been shown in a number of empirical studies to be one popular choice for a data stream environment Gama et al (2003), Kargupta & Park (2004) and Bifet et al (2009). This is due to two important reasons.…”

Section: Advantages Of the Fourier Classifier Over The Decision Tree mentioning

confidence: 99%

The incremental Fourier classifier: Leveraging the discrete Fourier transform for classifying high speed data streams

Kithulgoda

Pears

Naeem

2018

Expert Systems with Applications

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“…This approach can outperform both a standalone decision tree as well as a standalone classifier [141]. In further research on functional leaves the authors of [142] show that, for incremental learning models, naïve Bayes classifiers used as functional leaves improve the accuracy over the majority class approach. However, this cannot be a rule of thumb.…”

Section: Hoeffding Option Trees With Functional Leavesmentioning

confidence: 99%

Computational Inference and Control of Quality in Multimedia Services

Menkovski

2015

Springer Theses

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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...

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Accurate decision trees for mining high-speed data streams

Cited by 217 publications

References 5 publications

Stress-Testing Hoeffding Trees

Stress-Testing Hoeffding Trees

The incremental Fourier classifier: Leveraging the discrete Fourier transform for classifying high speed data streams

Computational Inference and Control of Quality in Multimedia Services

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