Parameterization reduction using soft set theory for better decision making

Abstract

Information science plays a vital role in each and every field of science and technology, but it is facing several difficulties to handle the data and information, a main problem is data uncertainty, several theories are dealing with uncertainty, soft set theory also do vital role to handle this uncertainty problem. This paper analysed soft set reduction and how a sample dataset is converted into binary valued information system, and also analysed how binary valued information can be used to reduce dimension of data to take better decisions.

A Real Time Application of Soft Set in Parameterization Reduction for Decision Making Problem

“…In other words U is universe in soft set and it is a parameterized family of subsets of universe U. for , F(e) may be considered as the set of e elements of the soft set (F, E) as the set of e approximate elements of soft set [1]. Ultimately, soft set is a non-crisp set Definition 2.…”

“…Ultimately, soft set is a non-crisp set Definition 2. (See [1]). Let R be a family of equivalence relations and let .…”

“…Let R be a family of equivalence relations and let . It is say that A is dispensable in R if IND(R) = IND(R -{A}) [1][2], [4]. Otherwise A is indispensable in R. The family R is Independent if each is indispensable in R. otherwise R is dependent.…”

“…But these theories are not much successful to deal with uncertainty because they all have their own difficulties. One reason for these difficulties may be due to the inadequacy of the parameterization tools [1]. Molodtsov [2] developed the concept of soft set theory as an effective mathematical tool for dealing with uncertainties and non-crisp data.…”

“…Each approximate description can divide in two parts. First one is a predicate part and second one is an approximate value set [1]. In classical mathematics, a mathematical model of an object is constructed and defines the notion of an exact solution of this model.…”