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On Runtime and Classification Performance of the Discretize-Optimize (DISCO) Classification Approach
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (DISCO)ORCID-id: 0000-0003-3461-7079
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013).
2018 (engelsk)Inngår i: Performance Evaluation Review, ISSN 0163-5999, E-ISSN 1557-9484, Vol. 46, nr 3, s. 167-170Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Using machine learning in high-speed networks for tasks such as flow classification typically requires either very resource efficient classification approaches, large amounts of computational resources, or specialized hardware. Here we provide a sketch of the discretize-optimize (DISCO) approach which can construct an extremely efficient classifier for low dimensional problems by combining feature selection, efficient discretization, novel bin placement, and lookup. As feature selection and discretization parameters are crucial, appropriate combinatorial optimization is an important aspect of the approach. A performance evaluation is performed for a YouTube classification task using a cellular traffic data set. The initial evaluation results show that the DISCO approach can move the Pareto boundary in the classification performance versus runtime trade-off by up to an order of magnitude compared to runtime optimized random forest and decision tree classifiers.

sted, utgiver, år, opplag, sider
New york, USA: Association for Computing Machinery (ACM), 2018. Vol. 46, nr 3, s. 167-170
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URN: urn:nbn:se:kau:diva-71213DOI: 10.1145/3308897.3308965OAI: oai:DiVA.org:kau-71213DiVA, id: diva2:1290337
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Knowledge FoundationTilgjengelig fra: 2019-02-20 Laget: 2019-02-20 Sist oppdatert: 2019-11-08bibliografisk kontrollert

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