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Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0003-4973-9255
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-0535-1761
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
2017 (English)In: Trends in Social Network Analysis: Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment / [ed] Rokia Missaoui, Talel Abdessalem, Matthieu Latapy, Cham, Switzerland: Springer, 2017, 229-252 p.Chapter in book (Refereed)
Abstract [en]

Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of the Very Fast Decision Tree (VFDT) algorithm. These results are compared with a theoretical analysis on the algorithm, indicating that energy consumption is affected by the parameters design and that it can be reduced significantly while maintaining accuracy.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2017. 229-252 p.
Series
Lectures Notes in Social Networks
Keyword [en]
Energy efficiency, Green computing, Very Fast Decision Tree, Big Data
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37924DOI: 10.1007/978-3-319-53420-6_10ISBN: 978-3-319-53419-0 (print)ISBN: 978-3-319-53420-6 (electronic)OAI: oai:DiVA.org:hj-37924DiVA: diva2:1160024
Funder
Knowledge Foundation, 20140032]
Available from: 2017-11-14 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

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Lavesson, Niklas

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