Performance Analysis of Rough Set–Based Hybrid Classification Systems in the Case of Missing Values
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–neares...
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| Veröffentlicht in: | Journal of Artificial Intelligence and Soft Computing Research 2021-10, Vol.11 (4), p.307-318 |
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| container_issue | 4 |
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| container_title | Journal of Artificial Intelligence and Soft Computing Research |
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| creator | Nowicki, Robert K. Seliga, Robert Żelasko, Dariusz Hayashi, Yoichi |
| description | 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. |
| doi_str_mv | 10.2478/jaiscr-2021-0018 |
| format | Article |
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| source | Walter De Gruyter: Open Access Journals |
| subjects | Classification Fuzzy logic Fuzzy set theory Fuzzy systems Hybrid systems Machine learning Neural networks Real numbers Support vector machines |
| title | Performance Analysis of Rough Set–Based Hybrid Classification Systems in the Case of Missing Values |
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