<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

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

Faculty of Computer Systems and Software Engineering University of Malaysia, Pahang, Lebuh Raya Tun Razak, Gambang 26300, Kuantan, Malaysia, College of Computer Science and Engineering Northwest Normal University, Lanzhou Gansu, 730070, China

Faculty of Computer Systems and Software Engineering University of Malaysia, Pahang, Lebuh Raya Tun Razak, Gambang 26300, Kuantan, Malaysia

International Journal of Applied Mathematics and Computer Science

{"article-title":"DFIS: A novel data filling approach for an incomplete soft set"}

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...

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

Data publikacji: 28 gru 2012

Zakres stron: 817 - 828

DOI: https://doi.org/10.2478/v10006-012-0060-3

This content is open access.