Energy modeling of Hoeffding tree ensembles
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| Název: | Energy modeling of Hoeffding tree ensembles |
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| Autoři: | García-Martín, E., Bifet, A., Lavesson, Niklas, Professor, 1976 |
| Zdroj: | Intelligent Data Analysis. 25(1):81-104 |
| Témata: | Data stream mining, Energy efficiency, Ensembles, GreenAI, Hoeffding trees, Energy utilization, Forestry, Adaptation methods, Algorithm design, Energy patterns, Predictive accuracy, Socio-ecological, State of the art, Substantial energy, Tree algorithms, Green computing |
| 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. |
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| Přístupová URL adresa: | https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-51923 https://doi.org/10.3233/IDA-194890 |
| Databáze: | SwePub |
Nájsť tento článok vo Web of Science | Abstrakt: | 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. |
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| ISSN: | 1088467X 15714128 |
| DOI: | 10.3233/IDA-194890 |