In our everyday lives, we are often confronted with a variety of issues relating to data with a high dimensionality. Irrelevant and superfluous data, as well as valuable data, are present in such problems. As a result, dimensionality reduction has a broad range of applications in data processing and analysis. In recent years, the problem of dimensionality reduction in a fuzzy situation has gotten a lot of attention from researchers. They have developed different methods and hypotheses to solve these types of problems. Some of these approaches are based on a probabilistic approach, while others are not. Fuzzy soft set theory is gaining popularity for seeking coherent and logical solutions to a variety of real-world problems involving complexity, imprecision, and vagueness. A theoretical analysis of intuitionistic fuzzy soft has been developed recently. When ambiguity due to vagueness is more complicated, the combination of intuitionistic fuzzy set and intuitionistic fuzzy soft set is more useful from an application standpoint. The definition of fuzzy soft set is described in this chapter as a hybridization of fuzzy set and soft set theory. A new technique for converting a soft set table into a fuzzy soft set table is proposed, which can be used in big data dimension reduction. As a hybridization of fuzzy set and soft set theory, fuzzy soft set and intuitionistic fuzzy soft set are described. Following Sanchez’s approach, new methods of applying fuzzy soft and intuitionistic soft sets are also defined in Medical Diagnosis.
Author (s) Details
Prof. D. S. Hooda
Former PVC, Kurukshetra University, Department of Mathematics, G. J. University of Science and Technology, Hisar- 125001, India.
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