Extraction of Knowledge from Uncertain Data Employing Weighted Bipolar and Neutrosophic Soft Sets

Abstract

The discovery of soft sets is accredited to Molodtsov. This theory can cope with difficult
circumstances with a lot of ambiguity, like those where deciding is hard. The bipolar soft set (BSS)
and neutrosophic soft set (NSS) are algebraic models that can be viewed as soft set expansions. The
BSS theory states that we weigh the pros and cons when deciding and NSS theory can handle belief
system ambiguity, contradiction, and lack of knowledge due to its truth and falsity membership
values. The concept of BSS and NSS are explained in comprehensive detail in this article. This
article examined the weighted bipolar soft set (WBSS) and the weighted neutrosophic soft set
(WNSS), as well as how to make accurate decisions under uncertain or inadequate information. A
detailed comparison of information extraction approaches using weighted bipolar and
neutrosophic soft sets may be lacking in the literature. These strategies may have been studied
separately, but there may be little research comparing their performance under different settings
and with diverse data. Filling this gap with a thorough and rigorous comparison study would help
comprehend these techniques' practical benefits and drawbacks.