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Energy Modeling of Hoeffding Tree Ensembles
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-4973-9255
Télécom ParisTech. (Data, Intelligence and Graphs (DIG) LTCI)
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
(English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128Article in journal (Refereed) Accepted
Abstract [en]

Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21 % on average, affecting accuracy by less than one percent on average.

National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-19151OAI: oai:DiVA.org:bth-19151DiVA, id: diva2:1388161
Funder
Knowledge Foundation, 20140032Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-01-28Bibliographically approved
In thesis
1. Energy Efficiency in Machine Learning: Approaches to Sustainable Data Stream Mining
Open this publication in new window or tab >>Energy Efficiency in Machine Learning: Approaches to Sustainable Data Stream Mining
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Energy efficiency in machine learning explores how to build machine learning algorithms and models with low computational and power requirements. Although energy consumption is starting to gain interest in the field of machine learning, still the majority of solutions focus on obtaining the highest predictive accuracy, without a clear focus on sustainability.

This thesis explores green machine learning, which builds on green computing and computer architecture to design sustainable and energy efficient machine learning algorithms. In particular, we investigate how to design machine learning algorithms that automatically learn from streaming data in an energy efficient manner.

We first illustrate how energy can be measured in the context of machine learning, in the form of a literature review and a procedure to create theoretical energy models. We use this knowledge to analyze the energy footprint of Hoeffding trees, presenting an energy model that maps the number of computations and memory accesses to the main functionalities of the algorithm. We also analyze the hardware events correlated to the execution of the algorithm, their functions and their hyper parameters.

The final contribution of the thesis is showcased by two novel extensions of Hoeffding tree algorithms, the Hoeffding tree with nmin adaptation and the Green Accelerated Hoeffding Tree. These solutions are able to reduce their energy consumption by twenty and thirty percent, with minimal effect on accuracy. This is achieved by setting an individual splitting criteria for each branch of the decision tree, spending more energy on the fast growing branches and saving energy on the rest.

This thesis shows the importance of evaluating energy consumption when designing machine learning algorithms, proving that we can design more energy efficient algorithms and still achieve competitive accuracy results.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2020. p. 267
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2
Keywords
machine learning, energy efficiency, data stream mining, green machine learning, edge computing
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18986 (URN)978-91-7295-396-3 (ISBN)
Public defence
2020-01-31, J1650, Blekinge Institute of Technology, Karlskrona, 13:15 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 20140032
Available from: 2019-12-03 Created: 2019-12-03 Last updated: 2020-02-26Bibliographically approved

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