Journal
ARTIFICIAL INTELLIGENCE REVIEW
Volume 52, Issue 3, Pages 1839-1872Publisher
SPRINGER
DOI: 10.1007/s10462-017-9592-0
Keywords
Parameter reduction; Normal parameter reduction; Soft set; Fuzzy soft set; Decision making
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Funding
- NNSFC [11461025, 11561023]
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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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