GRIMP: A machine‐learning method for improving groups of discriminating species in expert systems for vegetation classification. (1st February 2019)

• Record Type: Journal Article
• Authors: Tichý, Lubomír; Chytrý, Milan; Landucci, Flavia
• Editors: Podani, János
• Is Part Of: Journal of vegetation science. Volume 30:Number 1(2019:Jan.)
• Page Start: 5
• Page End: 17
• Publication Date: 2019-02-01
• Subjects: Plant ecology -- Periodicals; Plant communities -- Periodicals; Plant populations -- Periodicals; 581.7
• DOI: 10.1111/jvs.12696
• Languages: English
• ISSNs: 1100-9233

Abstract

Abstract: 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. Abstract : We present a new semi‐automatic machine‐learning method, GRIMP, for optimizing groups of species used for discriminating among vegetation types in expert systems. Optimized groups are very useful for the 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.