<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Performance Analysis of Rough Set–Based Hybrid Classification Systems in the Case of Missing Values

Department of Intelligent Computer Systems

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The paper presents a performance analysis of a selected few rough set–based classification systems. They are hybrid solutions designed to process information...

Performance Analysis of Rough Set–Based Hybrid Classification Systems in the Case of Missing Values

Online veröffentlicht: 08. Okt. 2021

Seitenbereich: 307 - 318

Eingereicht: 07. Jan. 2021

Akzeptiert: 23. Juli 2021

DOI: https://doi.org/10.2478/jaiscr-2021-0018

Schlüsselwörter
rough sets, support vector machines, fuzzy systems, neural networks

© 2021 Robert K. Nowicki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Performance Analysis of Rough Set–Based Hybrid Classification Systems in the Case of Missing Values

Online veröffentlicht: 08. Okt. 2021

Seitenbereich: 307 - 318

Eingereicht: 07. Jan. 2021

Akzeptiert: 23. Juli 2021

DOI: https://doi.org/10.2478/jaiscr-2021-0018

Schlüsselwörterrough sets, support vector machines, fuzzy systems, neural networks

© 2021 Robert K. Nowicki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
rough sets, support vector machines, fuzzy systems, neural networks