DFIS: A novel data filling approach for an incomplete soft set

• Autorzy: Qin H. , Ma X. , Herawan T. , Zain J. M.
• Języki publikacji: EN

Abstrakty

EN

The research on incomplete soft sets is an integral part of the research on soft sets and has been initiated recently. However, the existing approach for dealing with incomplete soft sets is only applicable to decision making and has low forecasting accuracy. In order to solve these problems, in this paper we propose a novel data filling approach for incomplete soft sets. The missing data are filled in terms of the association degree between the parameters when a stronger association exists between the parameters or in terms of the distribution of other available objects when no stronger association exists between the parameters. Data filling converts an incomplete soft set into a complete soft set, which makes the soft set applicable not only to decision making but also to other areas. The comparison results elaborated between the two approaches through UCI benchmark datasets illustrate that our approach outperforms the existing one with respect to the forecasting accuracy.

Słowa kluczowe

EN

soft sets; incomplete soft sets; data filling; association degree

PL

zbiór rozmyty; napełnianie danych

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2012
• Tom: Vol. 22, no. 4
• Strony: 817--828
• Opis fizyczny: Bibliogr. 48 poz., tab., wykr.

Twórcy

autor

Qin H.

autor

Ma X.

autor

Herawan T.

autor

Zain J. M.

• College of Computer Science and Engineering, Northwest Normal University, Lanzhou Gansu, 730070, China,

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