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

Yaya Liu; Keyun Qin; Chang Rao; Mahamuda Alhaji Mahamadu

International Journal of Applied Mathematics and Computer Science (2017)

  • Volume: 27, Issue: 1, page 157-167
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Yaya Liu, et al. "Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set." International Journal of Applied Mathematics and Computer Science 27.1 (2017): 157-167. <http://eudml.org/doc/288091>.

@article{YayaLiu2017,
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.},
author = {Yaya Liu, Keyun Qin, Chang Rao, Mahamuda Alhaji Mahamadu},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fuzzy soft set; incomplete fuzzy soft set; object-parameter approach; prediction; similarity measures},
language = {eng},
number = {1},
pages = {157-167},
title = {Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set},
url = {http://eudml.org/doc/288091},
volume = {27},
year = {2017},
}

TY - JOUR
AU - Yaya Liu
AU - Keyun Qin
AU - Chang Rao
AU - Mahamuda Alhaji Mahamadu
TI - Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set
JO - International Journal of Applied Mathematics and Computer Science
PY - 2017
VL - 27
IS - 1
SP - 157
EP - 167
AB - 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.
LA - eng
KW - fuzzy soft set; incomplete fuzzy soft set; object-parameter approach; prediction; similarity measures
UR - http://eudml.org/doc/288091
ER -

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