Authors:
Jarosław Szkoła
1
and
Piotr Artiemjew
2
Affiliations:
1
The Department of Computer Science, University of Rzeszow, 35-310 Rzeszow, Poland
;
2
Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Poland
Keyword(s):
Rough Sets, Granulation of Knowledge, Rough Inclusions, Standard Granulation, Estimation of the Approximation Degree, Sequential Neural Networks.
Abstract:
The paradigm of granular computing appeared from an idea proposed by L. A Zadeh, who assumed that a key element of data mining techniques is the grouping of objects using similarity measures. He assumed that similar objects could have similar decision classes. This assumption also guides other scientific streams such as reasoning by analogy, nearest neighbour method, and rough set methods. This assumption leads to the implication that grouped data, (granules) can be used to reduce the volume of decision systems while preserving their classification efficiency - internal knowledge. This hypothesis has been verified in practice - including in the works of Polkowski and Artiemjew (2007 - 2015) - where they use rough inclusions proposed by Polkowski and Skowron as an approximation tool - using the approximation scheme proposed by Polkowski. In this work, we present the application of sequential neural networks to estimate the degree of approximation of decision systems (the degree of red
uction in the size) based on the degree of indiscernibility of the decision system. We use the standard granulation method as a reference method. Pre-estimation of the degree of approximation is an important problem for the considered techniques, in the context of the possibility of their rapid application. This estimation allows the selection of optimal parameters without the need for a classification process.
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