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On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation

Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, 10-710 Olsztyn, Poland
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These authors contributed equally to this work.
Extended version of paper “Missing values absorbtion based on homogeneous granulation” presented at the 25th International Conference on Information and Software Technologies (ICIST 2019) held on 10–12 October 2019 in Vilnius, Lithuania.
Computers 2020, 9(1), 13; https://doi.org/10.3390/computers9010013
Received: 2 January 2020 / Revised: 10 February 2020 / Accepted: 12 February 2020 / Published: 15 February 2020
This paper is a continuation of works based on a previously developed new granulation method—homogeneous granulation. The most important new feature of this method compared to our previous ones is that there is no need to estimate optimal parameters. Approximation parameters are selected dynamically depending on the degree of homogeneity of decision classes. This makes the method fast and simple, which is an undoubted advantage despite the fact that it gives a slightly lower level of approximation to our other techniques. In this particular article, we are presenting its performance in the process of missing values absorption. We test selected strategies on synthetically damaged data from the UCI repository. The added value is to investigate the specific performance of our new granulation technique in absorbing missing values. The effectiveness of their absorption in the granulation process has been confirmed in our experiments.
Keywords: granular rough computing; missing values handling; homogeneous granulation granular rough computing; missing values handling; homogeneous granulation
MDPI and ACS Style

Artiemjew, P.; Ropiak, K.K. On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation. Computers 2020, 9, 13.

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