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Performance analysis of rough set–based hybrid classification systems in the case of missing values

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

Journal of Artificial Intelligence and Soft Computing Research

2021

Vol. 11, No. 4

307--318

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.

Rough support vector machine for classification with interval and incomplete data

Nowicki Robert K., Grzanek Konrad, Hayashi Yoichi

Journal of Artificial Intelligence and Soft Computing Research

2020

Vol. 10, No. 1

47--56

The paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.