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Hoeffding Trees with nmin adaptation
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0003-4973-9255
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0002-0535-1761
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0001-9947-1088
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.ORCID-id: 0000-0002-3118-5058
Vise andre og tillknytning
2018 (engelsk)Inngår i: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE, 2018, s. 70-79Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient.In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin pa- rameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.

sted, utgiver, år, opplag, sider
IEEE, 2018. s. 70-79
Serie
Proceedings of the International Conference on Data Science and Advanced Analytics, ISSN 2472-1573
Emneord [en]
data stream mining; green artificial intelligence; energy efficiency; hoeffding trees; energy aware machine learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-17207DOI: 10.1109/DSAA.2018.00017ISI: 000459238600008ISBN: 978-1-5386-5090-5 (tryckt)OAI: oai:DiVA.org:bth-17207DiVA, id: diva2:1260109
Konferanse
5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA), 1–4 October 2018, Turin
Forskningsfinansiär
Knowledge Foundation, 20140032Tilgjengelig fra: 2018-11-01 Laget: 2018-11-01 Sist oppdatert: 2019-04-05bibliografisk kontrollert

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García Martín, EvaLavesson, NiklasGrahn, HåkanCasalicchio, EmilianoBoeva, Veselka

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