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Training Instance Random Sampling Based Evidential Classification Forest Algorithms
University of Jinan, CHN .
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.ORCID iD: 0000-0001-5824-425X
University of Jinan, CHN .
2018 (English)In: 2018 21st International Conference on Information Fusion, FUSION 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 883-888Conference paper, Published paper (Refereed)
Abstract [en]

Modelling and handling epistemic uncertainty with belief function theory, different ways to learn classification forests from evidential training data are explored. In this paper, multiple base classifiers are learned on uncertain training subsets generated by training instance random sampling approach. For base classifier learning, with the tool of evidential likelihood function, gini impurity intervals of uncertain datasets are calculated for attribute splitting and consonant mass functions of labels are generated for leaf node prediction. The construction of gini impurity based belief binary classification tree is proposed and then compared with C4.5 belief classification tree. For base classifier combination strategy, both evidence combination method for consonant mass function outputs and majority voting method for precise label outputs are discussed. The performances of different proposed algorithms are compared and analysed with experiments on VCI Balance scale dataset. © 2018 ISIF

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 883-888
Keywords [en]
Binary trees, Forestry, Functions, Information fusion, Linguistics, Uncertainty analysis, Belief function theory, Binary classification, Classification trees, Epistemic uncertainties, Evidence combination, Likelihood functions, Multiple base classifiers, Training subsets, Classification (of information)
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:bth-17105DOI: 10.23919/ICIF.2018.8455427Scopus ID: 2-s2.0-85054089356ISBN: 9780996452762 (print)OAI: oai:DiVA.org:bth-17105DiVA, id: diva2:1255147
Conference
21st International Conference on Information Fusion, FUSION,Cambridge
Available from: 2018-10-11 Created: 2018-10-11 Last updated: 2018-10-12Bibliographically approved

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Sun, Bin

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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