- Tytuł:
- Performance analysis of rough set–based hybrid classification systems in the case of missing values
- Autorzy:
-
Nowicki, Robert K.
Seliga, Robert
Żelasko, Dariusz
Hayashi, Yoichi - Powiązania:
- https://bibliotekanauki.pl/articles/2031102.pdf
- Data publikacji:
- 2021
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
rough sets
support vector machine
fuzzy system
neural networks - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 4; 307-318
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł