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ArticleOriginal scientific text

Title

Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set

Authors 1, 1, 2, 3

Affiliations

  1. College of Mathematics, Southwest Jiaotong University, Chengdu 610031, Sichuan, PR China
  2. College of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, Sichuan, PR China
  3. Council for Scientific and Industrial Research, PO 132, Accra, Ghana

Abstract

The research on incomplete fuzzy soft sets is an integral part of the research on fuzzy soft sets and has been initiated recently. In this work, we first point out that an existing approach to predicting unknown data in an incomplete fuzzy soft set suffers from some limitations and then we propose an improved method. The hidden information between both objects and parameters revealed in our approach is more comprehensive. Furthermore, based on the similarity measures of fuzzy sets, a new adjustable object-parameter approach is proposed to predict unknown data in incomplete fuzzy soft sets. Data predicting converts an incomplete fuzzy soft set into a complete one, which makes the fuzzy soft set applicable not only to decision making but also to other areas. The compared results elaborated through rate exchange data sets illustrate that both our improved approach and the new adjustable object-parameter one outperform the existing method with respect to forecasting accuracy.

Keywords

ENG
POL
fuzzy soft set, incomplete fuzzy soft set, object parameter approach, prediction, similarity measures

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Additional information

PL: Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).

International Journal of Applied Mathematics and Computer Science
2017/27/1
Export to PBN, Opens in new tab
Pages:
157-167
Main language of publication
English
Published
2017

DOI: 10.1515/amcs-2017-0011

Exact and natural sciences