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Biol Open. 2018 Apr 11. pii: bio.031237. doi: 10.1242/bio.031237. [Epub ahead of print]

Selection of morphological features of pollen grains for chosen tree taxa.

Author information

1
Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, Poland.
2
Department of Applied Mathematics and Computer Science, University of Life Sciences in Lublin, Poland elzbieta.kubera@up.lublin.pl.
3
Department of Botany, University of Life Sciences in Lublin, Poland.

Abstract

The basis of aerobiological studies is to monitor airborne pollen concentrations and pollen season timing. This task is performed by appropriately trained staff and is difficult and time consuming.The goal of this research is to select morphological characteristics of grains that are the most discriminative for distinguishing between birch, hazel and alder taxa and are easy to determine automatically from microscope images. This selection is based on the split attributes of the J4.8 classification trees built for different subsets of features. Determining the discriminative features by this method, we provide specific rules for distinguishing between individual taxa, at the same time obtaining a high percentage of correct classification.The most discriminative among the 13 morphological characteristics studied are the following: number of pores, maximum axis, minimum axis, axes difference, maximum oncus width, number of lateral pores. The classification result of the tree based on this subset is better than the one built on the whole feature set and it is almost 94%. Therefore, selection of attributes before tree building is recommended.The classification results for the features easiest to obtain from the image, i.e. maximum axis, minimum axis, axes difference, and number of lateral pores, are only 2.09 pp lower than those obtained for the complete set, but 3.23 pp lower than the results obtained for the selected most discriminating attributes only .

KEYWORDS:

Attribute selection; Classification tree; Morphological features; Pollen grains identification

PMID:
29643087
DOI:
10.1242/bio.031237
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