GRIMP: A machine-learning method for improving groups of discriminating species in expert systems for vegetation classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F19%3A00107368" target="_blank" >RIV/00216224:14310/19:00107368 - isvavai.cz</a>

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/full/10.1111/jvs.12696" target="_blank" >https://onlinelibrary.wiley.com/doi/full/10.1111/jvs.12696</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/jvs.12696" target="_blank" >10.1111/jvs.12696</a>

Result language

angličtina

Original language name

GRIMP: A machine-learning method for improving groups of discriminating species in expert systems for vegetation classification

Original language description

Aims: Expert systems are increasingly popular tools for supervised classification of large datasets of vegetation-plot records, but their classification accuracy depends on the selection of proper species and species groups that can effectively discriminate vegetation types. Here, we present a new semi-automatic machine-learning method called GRIMP (GRoup IMProvement) to optimize groups of species used for discriminating among vegetation types in expert systems. We test its performance using a large set of vegetation-plot records. - Methods: We defined discriminating species groups as the groups that are unique to each vegetation type and provide optimal discrimination of this type against other types. The group of discriminating species of each vegetation type considerably overlaps with the group of diagnostic species of this type, but these two groups are not identical because not all diagnostic species have sufficient discriminating power. We developed the GRIMP iterative algorithm, which optimizes the groups of discriminating species to provide the most accurate vegetation classification, using a training set of a priori classified plot records. We tested this method by comparing classification accuracy before and after the GRIMP optimization of species groups using vegetation-plot records from the Czech Republic a priori classified to 39 phytosociological classes, and three initial sets of candidate discriminating species from different sources. - Results: The GRIMP algorithm improved the classification accuracy at the class level from 65% correctly classified plots in the test dataset before group optimization to 88% thereafter. The other plots were misclassified or unclassified, but misclassifications were reduced by adding further expert-based criteria considering dominant growth forms. - Conclusions: GRIMP-optimized groups of discriminating species are very useful for semi-automatic construction of expert systems for vegetation classification. Such expert systems can be developed from an a priori unsupervised or expert-based classification of at least some vegetation plots.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10611 - Plant sciences, botany

Project

<a href="/en/project/GA17-15168S" target="_blank" >GA17-15168S: Next generation expert systems for vegetation classification on a continental scale</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Vegetation Science

ISSN

1100-9233

e-ISSN

1654-1103

Volume of the periodical

30

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

5-17

UT code for WoS article

000459812100002

EID of the result in the Scopus database

2-s2.0-85060890560