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On Runtime and Classification Performance of the Discretize-Optimize (DISCO) Classification Approach
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0003-3461-7079
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
2018 (English)In: Performance Evaulaton Review, Vol. 46, no 3, p. 167-170Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
New york, USA, 2018. Vol. 46, no 3, p. 167-170
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-71213DOI: 10.1145/3308897.3308965OAI: oai:DiVA.org:kau-71213DiVA, id: diva2:1290337
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HITS, 4707
Funder
Knowledge FoundationAvailable from: 2019-02-20 Created: 2019-02-20 Last updated: 2019-03-14Bibliographically approved

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Garcia, JohanKorhonen, Topi

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Citation style
  • apa
  • harvard1
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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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