Új Nemzeti Kiválóság Program(ÚNKP-18-4) Támogató: EMMI
OTKA(K124055)
Nemzetköziesítés, oktatói, kutatói és hallgatói utánpótlás megteremtése, a tudás és
technológiai ...(EFOP-3.6.1-16-2016-00017) Támogató: EFOP
KKV-k nemzetközi versenyképességét támogató szolgáltatások fejlesztése(GINOP-2.3.4-15-2016-00003)
Támogató: OTKA
If the antecedents of a fuzzy classification method are derived from pictures or measured
data, it might have too many dimensions to handle. A classification scheme based on
such data has to apply a careful selection or processing of the measured results:
either a sampling, re-sampling is necessary or the usage of functions, transformations
that reduce the long, high dimensional observed data vector or matrix into a single
point or to a low number of points. Wavelet analysis can be useful in such cases in
two ways.As the number of resulting points of the wavelet analysis is approximately
half at each filters, a consecutive application of wavelet transform can compress
the measurement data, thus reducing the dimensionality of the signal, i.e., the antecedent.
An SHDSL telecommunication line evaluation is used to demonstrate this type of applicability,
wavelets help in this case to overcome the problem of a one dimensional signal sampling.In
the case of using statistical functions, like mean, variance, gradient, edge density,
Shannon or Renyi entropies for the extraction of the information from a picture or
a measured data set, and they don not produce enough information for performing the
classification well enough, one or two consecutive steps of wavelet analysis and applying
the same functions for the thus resulting data can extend the number of antecedents,
and can distill such parameters that were invisible for these functions in the original
data set. We give two examples, two fuzzy classification schemes to show the improvement
caused by wavelet analysis: a measured surface of a combustion engine cylinder and
a colonoscopy picture. In the case of the first example the wear degree is to be determine,
in the case of the second one, the roundish polyp content of the picture. In the first
case the applied statistical functions are Renyi entropy differences, the structural
entropies, in the second case mean, standard deviation, Canny filtered edge density,
gradients and the entropies.In all the examples stabilized KH rule interpolation was
used to treat sparse rulebases.The preliminary version of this paper was presented
at the 3rd Conference on Information Technology, Systems Research and Computational
Physics, 2-5 July 2018, Cracow, Poland [1].
