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

Abstract

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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.

How to cite

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Hongwu 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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