Performance analysis of rough set–based hybrid classification systems in the case of missing values
Authors:
- Robert K. Nowicki,
- Robert Seliga,
- Dariusz Żelasko,
- Yoichi Hayashi
Abstract
The paper presents a performance analysis of a selected few rough set–based classification systems. They are hybrid solutions designed to process information with missing values. Rough set-–based classification systems combine various classification methods, such as support vector machines, k–nearest neighbour, fuzzy systems, and neural networks with the rough set theory. When all input values take the form of real numbers, and they are available, the structure of the classifier returns to a non–rough set version. The performance of the four systems has been analysed based on the classification results obtained for benchmark databases downloaded from the machine learning repository of the University of California at Irvine.
- Record ID
- CUT0fa1d202152c4d26b9104f18b4cdce44
- Publication categories
- ;
- Author
- Journal series
- Journal of Artificial Intelligence and Soft Computing Research, ISSN 2083-2567, e-ISSN 2449-6499
- Issue year
- 2021
- Vol
- 11
- No
- 4
- Pages
- 307-318
- Other elements of collation
- tab.; wykr.; Bibliografia (na s.) - 316-318; Bibliografia (liczba pozycji) - 31; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 11, No. 4
- Keywords in English
- rough sets, support vector machines, fuzzy systems, neural networks
- DOI
- DOI:10.2478/jaiscr-2021-0018 Opening in a new tab
- URL
- https://sciendo.com/article/10.2478/jaiscr-2021-0018 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Publication indicators
- = 2
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT0fa1d202152c4d26b9104f18b4cdce44/
- URN
urn:pkr-prod:CUT0fa1d202152c4d26b9104f18b4cdce44
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.