Data analysis approaches of soft sets under incomplete information

Knowledge-Based SystemsPublished 21 April 2008

Yan Zou, Zhi Xiao

DOI10.1016/j.knosys.2008.04.004

Source URL

Citations418

SJR quartileQ1

SJR score1.93

SNIP2.34

TL;DR

Results of comparison show that comparing to other approaches for dealing with incomplete data, these approaches presented in this paper are preferable for reflecting actual states of incomplete data in soft sets.

Abstract

In view of the particularity of the value domains of mapping functions in soft sets, this paper presents data analysis approaches of soft sets under incomplete information. For standard soft sets, the decision value of an object with incomplete information is calculated by weighted-average of all possible choice values of the object, and the weight of each possible choice value is decided by the distribution of other objects. For fuzzy soft sets, incomplete data will be predicted based on the method of average-probability. Results of comparison show that comparing to other approaches for dealing with incomplete data, these approaches presented in this paper are preferable for reflecting actual states of incomplete data in soft sets. At last, an example is provided to illuminate the practicability and validity of the data analysis approach of soft sets under incomplete information.

Keywords

Computer ScienceDecision Sciences

Computers & Mathematics with ApplicationsSoft set theory—First results

4,633 Citations1999D.A. Molodtsov

The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the the theory, and to discuss some problems of the future.

Computers & Mathematics with ApplicationsSoft set theory

2,252 Citations2003Pabitra Kumar Maji, Ranjit Biswas +1 more

The authors define equality of two soft sets, subset and super set of a soft set, complement of asoft set, null soft set and absolute soft set with examples and De Morgan's laws and a number of results are verified in soft set theory.

Computers & Mathematics with ApplicationsAn application of soft sets in a decision making problem

1,700 Citations2002Pabitra Kumar Maji, Arijit Roy +1 more

Information SciencesSoft sets and soft groups

1,095 Citations2007Hacı Aktaş, Naim Çağman

The basic properties of soft sets are introduced, and compare soft sets to the related concepts of fuzzy sets and rough sets, and a definition of soft groups is given.

Journal of Computational and Applied MathematicsA fuzzy soft set theoretic approach to decision making problems

935 Citations2007Arijit Roy, Pabitra Kumar Maji

A novel method of object recognition from an imprecise multiobserver data has been presented here and involves construction of a Comparison Table from a fuzzy soft set in a parametric sense for decision making.

Elsevier eBooksUNKNOWN ATTRIBUTE VALUES IN INDUCTION

303 Citations1989J. R. Quinlan

This paper compares the effectiveness of several approaches to the development and use of decision tree classifiers as measured by their performance on a collection of datasets.

Accelerated quantification of Bayesian networks with incomplete data

55 Citations1995Bo Thiesson

This paper considers statistical batch learning of the probability tables on the basis of incomplete data and expert knowledge and proposes a new class of models that allows a great variety of local functional restrictions to be imposed on the statistical model.

Journal of Intelligent Information SystemsMining Informative Rule Set for Prediction

13 Citations2004Jiuyong Li, Hong Shen +1 more

This paper presents an algorithm to directly generate the informative rule set without generating all frequent itemsets first that accesses the database less frequently than other direct methods and shows experimentally that the informativerule set is much smaller and can be generated more efficiently than both the association rule set and non-redundant association ruleSet.