Kubik-Komar Agnieszka, Kubera Elżbieta, Piotrowska-Weryszko Krystyna
University of Life Sciences in Lublin, Department of Applied Mathematics and Computer Science, Akademicka 13, 20-950 Lublin, Poland.
University of Life Sciences in Lublin, Department of Applied Mathematics and Computer Science, Akademicka 13, 20-950 Lublin, Poland
Biol Open. 2018 Apr 30;7(5):bio031237. doi: 10.1242/bio.031237.
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, and 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.
空气生物学研究的基础是监测空气中花粉浓度和花粉季节时间。这项任务由经过适当培训的人员执行，既困难又耗时。本研究的目标是选择对区分桦树、榛树和桤木分类群最具判别力且易于从显微镜图像自动确定的花粉粒形态特征。这种选择基于为不同特征子集构建的J4.8分类树的分裂属性。通过这种方法确定判别特征，我们提供了区分各个分类群的具体规则，同时获得了较高的正确分类百分比。在所研究的13个形态特征中，最具判别力的如下：孔的数量、最大轴、最小轴、轴差、最大瘤宽度和侧孔数量。基于此子集构建的树的分类结果优于基于整个特征集构建的树，几乎达到94%。因此，建议在构建树之前选择属性。从图像中最容易获得的特征，即最大轴、最小轴、轴差和侧孔数量的分类结果，仅比完整集的结果低2.09个百分点，但比仅选择最具判别力的属性所获得的结果低3.23个百分点。
