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Dynamic algorithm selection for machine learning on time series
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

We present a software that can dynamically determine what machine learning algorithm is best to use in a certain situation given predefined traits. The produced software uses ideal conditions to exemplify how such a solution could function. The software is designed to train a selection algorithm that can predict the behavior of the specified testing algorithms to derive which among them is the best. The software is used to summarize and evaluate a collection of selection algorithm predictions to determine  which testing algorithm was the best during that entire period. The goal of this project is to provide a prediction evaluation software solution can lead towards a realistic implementation.

Place, publisher, year, edition, pages
2019. , p. 55
Keywords [en]
Computer science, Machine learning, Selection, time series, Algorithm selection
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kau:diva-72576OAI: oai:DiVA.org:kau-72576DiVA, id: diva2:1325154
External cooperation
CGI
Subject / course
Computer Science
Educational program
Engineering: Computer Engineering (300 ECTS credits)
Presentation
2019-06-04, 10:05 (Swedish)
Supervisors
Examiners
Available from: 2019-06-26 Created: 2019-06-14 Last updated: 2019-06-26Bibliographically approved

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fulltext(929 kB)20 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf