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Ensemble Multisensor Data Using State-of-the-Art Classification Methods
Univ Johannesburg, Dept Elect & Elect Engn Sci, ZA-2006 Johannesburg, South Africa. CSIR, Meraka Inst, ZA-0001 Pretoria, South Africa..ORCID iD: 0000-0002-3452-9581
Univ Johannesburg, Dept Elect & Elect Engn Sci, ZA-2006 Johannesburg, South Africa. CSIR, Meraka Inst, ZA-0001 Pretoria, South Africa..ORCID iD: 0009-0006-6443-4483
2013 (English)In: AFRICON, 2013, IEEE , 2013, p. 1255-1260Conference paper, Published paper (Refereed)
Abstract [en]

Detection and identification from sensing image is a common task for many applications. In order to improve the performance of detection and identification the use of multiple classifier combination is demonstrated and evaluated in the paper using two industrial image datasets. Experiments show that multiple classifier combination can improve the performance of image classification and image detection and identification with boosting and bagging achieve higher accuracy rates. Accordingly, good performance is consistently derived from static parallel systems.

Place, publisher, year, edition, pages
IEEE , 2013. p. 1255-1260
Series
Africon, ISSN 2153-0025
Keywords [en]
multiple classifiers, ensemble systems, machine learning, sensor data, target tracking
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:mau:diva-64139DOI: 10.1109/AFRCON.2013.6757711ISI: 000352082700266Scopus ID: 2-s2.0-84897430753ISBN: 978-1-4673-5940-5 (print)OAI: oai:DiVA.org:mau-64139DiVA, id: diva2:1817682
Conference
AFRICON Conference, SEP 09-12, 2013, IEEE Reg 8, Pointe aux Piments, MAURITIUS
Available from: 2023-12-07 Created: 2023-12-07 Last updated: 2023-12-07Bibliographically approved

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Mekuria, Fisseha

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Twala, BhekisiphoMekuria, Fisseha
Computer Vision and Robotics (Autonomous Systems)

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CiteExportLink to record
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  • apa
  • ieee
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  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
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