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

Hongwu Qin; Xiuqin Ma; Tutut Herawan; Jasni Mohamad Zain

International Journal of Applied Mathematics and Computer Science (2012)

- Volume: 22, Issue: 4, page 817-828
- ISSN: 1641-876X

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topHongwu Qin, et al. "DFIS: A novel data filling approach for an incomplete soft set." International Journal of Applied Mathematics and Computer Science 22.4 (2012): 817-828. <http://eudml.org/doc/244568>.

@article{HongwuQin2012,

abstract = {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.},

author = {Hongwu Qin, Xiuqin Ma, Tutut Herawan, Jasni Mohamad Zain},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {soft sets; incomplete soft sets; data filling; association degree},

language = {eng},

number = {4},

pages = {817-828},

title = {DFIS: A novel data filling approach for an incomplete soft set},

url = {http://eudml.org/doc/244568},

volume = {22},

year = {2012},

}

TY - JOUR

AU - Hongwu Qin

AU - Xiuqin Ma

AU - Tutut Herawan

AU - Jasni Mohamad Zain

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

JO - International Journal of Applied Mathematics and Computer Science

PY - 2012

VL - 22

IS - 4

SP - 817

EP - 828

AB - 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.

LA - eng

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

UR - http://eudml.org/doc/244568

ER -

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