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@article{1524877,
author = {Tichý, Lubomír and Chytrý, Milan and Landucci, Flavia},
article_location = {Hoboken},
article_number = {1},
doi = {http://dx.doi.org/10.1111/jvs.12696},
keywords = {diagnostic species group; discriminating species group; expert system; phytosociology; plant community; vegetation classification; vegetation-plot database; vegetation survey},
language = {eng},
issn = {1100-9233},
journal = {Journal of Vegetation Science},
title = {GRIMP: A machine-learning method for improving groups of discriminating species in expert systems for vegetation classification},
url = {https://onlinelibrary.wiley.com/doi/full/10.1111/jvs.12696},
volume = {30},
year = {2019}
}
TY - JOUR
ID - 1524877
AU - Tichý, Lubomír - Chytrý, Milan - Landucci, Flavia
PY - 2019
TI - GRIMP: A machine-learning method for improving groups of discriminating species in expert systems for vegetation classification
JF - Journal of Vegetation Science
VL - 30
IS - 1
SP - 5-17
EP - 5-17
PB - Wiley
SN - 11009233
KW - diagnostic species group
KW - discriminating species group
KW - expert system
KW - phytosociology
KW - plant community
KW - vegetation classification
KW - vegetation-plot database
KW - vegetation survey
UR - https://onlinelibrary.wiley.com/doi/full/10.1111/jvs.12696
L2 - https://onlinelibrary.wiley.com/doi/full/10.1111/jvs.12696
N2 - 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.
ER -
TICHÝ, Lubomír, Milan CHYTRÝ and Flavia LANDUCCI. GRIMP: A machine-learning method for improving groups of discriminating species in expert systems for vegetation classification. \textit{Journal of Vegetation Science}. Hoboken: Wiley, 2019, vol.~30, No~1, p.~5-17. ISSN~1100-9233. Available from: https://dx.doi.org/10.1111/jvs.12696.
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