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Funded by the National Science Centre, Poland under
CHIST-ERA programme (Grant no. 2018/28/Z/ST6/00563).
The work was co-financed by the Lublin University of Technology Scientific Fund: FD-ITIT-KIER.
Materiał konferencyjny:
TAK
Nazwa konferencji:
IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2021
Classification of objects in empirical data, especially in biological sciences, is a very complex process and has been a big challenge for researchers who do not specialize in data analysis. There fore, in this study, we present a comprehensive summary of selected classifiers operating on both exact and fuzzy numbers. The results of performance of specific classifiers are compared on the example of a unique set of empirical data on changes in the behavior of animals in response to environmental factors. This is one of the key challenges in ecological research and it is strictly related to ecosystem changes caused by climate change. Nowadays, changes in behavior are a very popular topic of research because as a result of the COVID-19 pandemic and lower activity of people (lockdown effect). There fore, various unusual reactions of wild animals were found around the world.
A detailed compilation of research results, shortcomings, and
strengths of various classification methods may be a compendium
of knowledge for biologists and other practitioners as well as
researchers working with empirical data.
