Softmax function based neutrosophic aggregation operators and application in multi-attribute decision making problem - Totan Garai; Shyamal Dalapati; Florentin Smarandache (Neutrosophic Sets and Systems, 2023)
The softmax function is a well-known generalization of the logistic function. It has been extensively applied in various probabilistic classification methods such as softmax regression, linear discriminant analysis, naive Bayes classifiers, and artificial neural networks. Inspired by the advantages of the softmax function, we have developed the softmax function-based single-valued neutrosophic aggregation operators. Then we have es tablished some essential properties of aggregation operators based on the softmax function with the neutrosophic set. Additionally, we have defined a multi-attribute decision-making method based on the proposed aggregation operators. Using the proposed MCDM method, we have developed a novel algorithm. This algorithm helps to examine FD-risk assessment problems. Also, the proposed algorithm process is a reasonable strategy for the decision-making problem. It is easy to recognize when choosing a neutrosophic set of information for a practical decision problem. We used this proposed MADM method to exercise a realistic MADM problem with neutrosophic information. Finally, we have considered one numerical illustration to show the validity and reliability of the proposed methods.