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Recently, approaches based on soft sets for attribute selection has also been proposed to reduce the execution time [13, 20].
Using soft sets as the granulation structures, Feng et al. [21] initiated soft approximation spaces and soft rough sets, which generalize Pawlak's rough sets based on soft sets.
Regarding maximal association rule mining based on soft sets, we point out the drawbacks of some existing definitions and offer some way to rectify the problem.
Using soft sets as the granulation structures, Feng et al. [8] defined soft approximation spaces, soft rough approximations and soft rough sets, which are generalizations of Pawlak's rough set model based on soft sets.
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A comparison between transmit power setting based on soft decisions (corresponding to SS) and transmit power setting based on hard decisions (corresponding to OSA) was shown [13].
To address decision making problems based on fuzzy soft sets, Feng et al. introduced the concept of level soft sets of fuzzy soft sets and initiated an adjustable decision-making scheme using fuzzy soft sets [6].
For the part of lung tumors located away from thoracic vertebra, the set-up error was calculated based on soft tissue.
The proposed GB conversion method, as presented here, is based on soft correspondence between the source and target vectors, obtained by using a parallel training set.
Literature [4] also considers the blind recognition of coding parameters based on soft decisions.
The rest, she said, was based on soft tissue depth measurements and other forensic methodology.
Therefore, an approach based on soft dictionary matching and rules becomes a natural choice.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com