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

2021; Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology; Volume: 11; Issue: 4 Linguagem: Inglês

10.2478/jaiscr-2021-0018

2449-6499

Robert Nowicki, Robert Seliga, Dariusz Żelasko, Yoichi Hayashi,

Imbalanced Data Classification Techniques

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.