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Paper details
Number 1 - March 2017
Volume 27 - 2017
Object-parameter approaches to predicting unknown data in an incomplete fuzzy soft set
Yaya Liu, Keyun Qin, Chang Rao, Mahamuda Alhaji Mahamadu
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
fuzzy soft set, incomplete fuzzy soft set, object-parameter approach, prediction, similarity measures