A Generalization of Fuzzy Soft Max-Min Decision-Making Method and Its Application to a Performance-Based Value Assignment in Image Denoising

Abstract

Latterly, the fuzzy soft max-min decision-making method denoted by FSMmDM and provided in [Çağman, N., Enginoǧlu, S., Fuzzy soft matrix theory and its application in decision making, Iranian Journal of Fuzzy Systems, 2012, 9(1), 109-119] has been configured via fuzzy parameterized fuzzy soft matrices (matrices) by Enginoğlu and Memiş [A configuration of some soft decision-making algorithms via matrices, Cumhuriyet Science Journal, 2018, 39(4), 871-881], faithfully to the original. Although this configured method denoted by CE12 and constructed by and-product/or-product (CE12a/CE12o) is useful in decisionmaking, the method should be made more attractive in terms of time and complexity in the event that a large amount of data is processed. In this paper, we propose two algorithms denoted by EMC19a and EMC19o and being new generalisations of FSMmDM. Moreover, we prove that EMC19a accept CE12a as a special case in the event that the first rows of the-matrices are binary. Afterwards, we compare the running times of these algorithms. The results show that EMC19a and EMC19o outperform CE12a and CE12o, respectively, in any number of data. We then apply EMC19o to a decision-making problem in image denoising. Finally, we discuss the need for further research.

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