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Using aggregation functions to model human judgements of species diversity

Beliakov, Gleb, James, Simon and Nimmo, Dale G. 2015, Using aggregation functions to model human judgements of species diversity, Information sciences, vol. 306, pp. 21-33, doi: 10.1016/j.ins.2015.02.013.

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Title Using aggregation functions to model human judgements of species diversity
Author(s) Beliakov, GlebORCID iD for Beliakov, Gleb orcid.org/0000-0002-9841-5292
James, SimonORCID iD for James, Simon orcid.org/0000-0003-1150-0628
Nimmo, Dale G.
Journal name Information sciences
Volume number 306
Start page 21
End page 33
Total pages 13
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-06-10
ISSN 0020-0255
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Computer Science
Aggregation functions
Weights learning
Bonferroni mean
Species diversity
Ecological indices
CONSUMERS GUIDE
CONSENSUS
EVENNESS
INDEXES
Summary In environmental ecology, diversity indices attempt to capture both the number of species in a community and the relative abundance of each. Many indices have been proposed for quantifying diversity, often based on calculations of dominance, equity and entropy from other research fields. Here we use linear fitting techniques to investigate the use of aggregation functions, both for evaluating the relative biodiversity of different ecological communities, and for understanding human tendencies when making intuitive diversity comparisons. The dataset we use was obtained from an online exercise where individuals were asked to compare hypothetical communities in terms of diversity and importance for conservation.
Language eng
DOI 10.1016/j.ins.2015.02.013
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
050202 Conservation and Biodiversity
Socio Economic Objective 960805 Flora
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074244

Document type: Journal Article
Collection: School of Information Technology
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Created: Fri, 13 Nov 2015, 16:07:15 EST

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