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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
With the advent of research into Granular Computing,
in particular information granules, the way of thinking
about data has changed gradually. Researchers and practitioners
do not consider only their specific properties, but also try to
look at the data in a more general way, closer to the way
people think. This kind of knowledge representation is expressed
particularly in approaches based on linguistic modeling or fuzzy
techniques such as fuzzy clustering, but also newer approaches
related to the explanation of how artificial intelligence works on
these data (so-called explainable artificial intelligence). There fore,
especially important from the point of view of the methodology
of data research is an attempt to understand their potential
as information granules. Such a kind of approach to data
presentation and analysis may introduce considerations of a
higher, more general level of abstraction, while at the same time
reliably describing the network of relationships between the data
and the observed information granules. In this study, we tackle
this topic with particular emphasis on the problem of choosing a
predictive model. In a series of numerical experiments based on
both artificially generated data, ecological data on changes in bird
arrival dates in the context of climate change, and COVID-19
infections data we demonstrate the effectiveness of the proposed
approach built with a novel application of information potential
granules.
