Rough support vector machine for classification with interval and incomplete data

• Autorzy: Nowicki Robert K. , Grzanek Konrad , Hayashi Yoichi

Identyfikatory

DOI

10.2478/jaiscr-2020-0004

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

support vector machine; rough sets; missing features; interval data; three–way decision

PL

maszyna wektorów nośnych; dane interwałowe

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2020
• Tom: Vol. 10, No. 1
• Strony: 47--56
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Nowicki Robert K.

• Department of Computer Engineering, Częstochowa University of Technology, 42–200 Częstochowa, Poland

autor

Grzanek Konrad

• Information Technology Institute, University of Social Science, Łodź, Poland
• Clark University, Worcester, MA 01610, USA

autor

Hayashi Yoichi

• Department of Computer Science, Meiji University, Kawasaki 214-8571 Japan

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).