Mid Sweden University

miun.sePublications
Change search
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
A Study on Confidence: an Unsupervised Multi-Agent Machine Learning Experiment
2021 (English)In: IEEE design & test, ISSN 2168-2356, E-ISSN 2168-2364Article in journal (Refereed) Published
Abstract [en]

Computational Self-Awareness (CSA) is a growing field that has been applied to various applications, which often uses Machine Learning (ML). One of the key metrics for assessing the quality of both CSA and ML systems is confidence, which has been used in many applications recently. Confidence has shown a great promise in improving systems’ performance, in particular regarding the reliability of operations. However, from an engineering point of view, the nature of confidence as a metric has been an open question. Understanding the nature of confidence can help the better usage of the concept and, consequently, the design of better systems. Uncovering the true nature of confidence, however, is not currently within our reach. Therefore, in this work, we take one step in that direction by designing a socially-inspired experiment to investigate the nature of confidence in the context of (self-)learning. Our experiment shows that among the two candidates discussed in the literature, probability is a better metric for confidence. This observation sheds light on this open question and marks an entry point for further investigating the concept of confidence as a metric in ML and CSA. IEEE

Place, publisher, year, edition, pages
IEEE Computer Society , 2021.
Keywords [en]
Color, Computational self-awareness, Confidence, Image color analysis, Machine learning, Measurement, Monitoring, Multi-agent systems, Observation, Self-aware, Support vector machines, Unsupervised ML, Multi agent systems, Entry point, Improving systems, Ml systems, Multi agent, Self awareness
Identifiers
URN: urn:nbn:se:miun:diva-43422DOI: 10.1109/MDAT.2021.3078341Scopus ID: 2-s2.0-85105872950OAI: oai:DiVA.org:miun-43422DiVA, id: diva2:1604106
Available from: 2021-10-18 Created: 2021-10-18 Last updated: 2021-10-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Jantsch, A.
In the same journal
IEEE design & test

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 52 hits
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