Energy Modeling of Hoeffding Tree Ensembles
| Autor: | Albert Bifet, Niklas Lavesson, Eva García-Martín |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Energy utilization
Mathematical optimization Computer science Energy patterns 02 engineering and technology State of the art Theoretical Computer Science Reduction (complexity) Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering Socio-ecological Edge computing Adaptation methods Green computing Computer Sciences Predictive accuracy Energy modeling Forestry Energy consumption Tree algorithms Tree (data structure) Energy efficiency Algorithm design Datavetenskap (datalogi) Substantial energy 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Energy (signal processing) |
| Popis: | 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. © 2021 - IOS Press. All rights reserved. open access |
| Databáze: | OpenAIRE |
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