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A survey of parameter reduction of soft sets and corresponding algorithms

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Abstract

As is well known, soft set theory can have a bearing on making decisions in many fields. Particularly important is parameter reduction of soft sets, an essential topic both for information sciences and artificial intelligence. Parameter reduction studies the largest pruning of the amount of parameters that define a given soft set without changing its original choice objects. Therefore it can spare computationally costly tests in the decision making process. In the present article, we review some different algorithms of parameter reduction based on some types of (fuzzy) soft sets. Finally, we compare these algorithms to emphasize their respective advantages and disadvantages, and give some examples to illustrate their differences.

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Acknowledgements

The authors are extremely grateful to the editor and the referees for their valuable comments and helpful suggestions which help to improve the presentation of this paper. This research was supported by NNSFC (11461025 and 11561023).

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  1. Department of Mathematics, Hubei University for Nationalities, Enshi, 445000, People’s Republic of China

    Jianming Zhan

  2. BORDA Research Unit and Multidisciplinary Institute of Enterprise (IME), University of Salamanca, 37007, Salamanca, Spain

    José Carlos R. Alcantud

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  1. Jianming Zhan
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Zhan, J., Alcantud, J.C.R. A survey of parameter reduction of soft sets and corresponding algorithms. Artif Intell Rev 52, 1839–1872 (2019). https://doi.org/10.1007/s10462-017-9592-0

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