title: A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An application in medical diagnosis.
abstract: Recently, fuzzy soft sets-based decision making has attracted more and more interest. Although plenty of works have been done, they cannot provide the uncertainty or certainty of their results. To manage uncertainty is one of the most important and toughest tasks of decision making especially in medicine. In this study, we improve the performance of reducing uncertainty and raising the choice decision level in fuzzy soft set-based decision making.
  
  
  
  We make use of two appropriate tools (ambiguity measure and Dempster-Shafer theory of evidence) to improve fuzzy soft set-based decision making. Our proposed approach consists of three procedures: primarily, the uncertainty degree of each parameter is obtained by using ambiguity measure; next, the suitable basic probability assignment with respect to each parameter (or evidence) is constructed based on the uncertainty degree of each parameter obtained in the first step; in the end, the classical Dempster's combination rule is applied to aggregate independent evidences into the collective evidence, by which the candidate alternatives are ranked and the best alternative will be obtained.
  
  
  
  We compare the results of our proposed method with the recent relative works. Through employing our presented approach, in Example 5, the belief measure of the uncertainty falls to 0.0051 from 0.0751; in Example 6, the belief measure of the uncertainty drops to 0.0086 from 0.0547; in Example 7, the belief measure of the uncertainty falls to 0.0847 from 0.1647; in application, the belief measure of the uncertainty drops 0.0001 from 0.0069.
  
  
  
  Three numerical examples and an application in medical diagnosis are provided to demonstrate adequately that, on the one hand, our proposed method is feasible and efficient; on the other hand, our proposed method can reduce uncertainty caused by people's subjective cognition and raise the choice decision level with the best performance.
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China; School of HanHong, Southwest University, Chongqing 400715, China.
authors.forename: Jianwei
authors.initials: J
authors.lastname: Wang
authors.affiliation: Big Data Decision Institute, Jinan University, Tianhe, Guangzhou 510632, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Hu
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China.
authors.forename: Fuyuan
authors.initials: F
authors.lastname: Xiao
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China; Center for Quantitative Sciences, Vanderbilt University School of Medicine, Vanderbilt University, Nashville, TN 37235, USA.
authors.forename: Xinyang
authors.initials: X
authors.lastname: Deng
authors.affiliation: School of Computer and Information Science, Southwest University, Chongqing 400715, China; Big Data Decision Institute, Jinan University, Tianhe, Guangzhou 510632, China; Institute of Integrated Automation, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xian, Shaanxi 710049, China. Electronic address: prof.deng@hotmail.com.
authors.forename: Yong
authors.initials: Y
authors.lastname: Deng
mesh_terms: D003657:Decision Making; D003661:Decision Support Techniques; D017143:Fuzzy Logic; D006801:Humans; D011336:Probability; D035501:Uncertainty
keywords: Ambiguity measure; Belief function; Decision making; Dempster–Shafer evidence theory; Fuzzy soft set; Information fusion; Medical diagnosis; Target recognition