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Energy Efficiency A...
Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm
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- García Martín, Eva (author)
- Blekinge Tekniska Högskola,Institutionen för datalogi och datorsystemteknik,Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik
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- Lavesson, Niklas (author)
- Jönköping University,Blekinge Tekniska Högskola,Institutionen för datalogi och datorsystemteknik,Jönköping AI Lab (JAIL),Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik
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- Grahn, Håkan (author)
- Blekinge Tekniska Högskola,Institutionen för datalogi och datorsystemteknik,Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik
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(creator_code:org_t)
- 2017-04-30
- 2017
- English.
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In: Trends in Social Network Analysis. - Cham, Switzerland : Springer. - 9783319534190 - 9783319534206 ; , s. 229-252
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Energy efficiency
- Green computing
- Very Fast Decision Tree
- Big Data
Publication and Content Type
- ref (subject category)
- kap (subject category)
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