###### Abstract
In contrast, in our proposed approach, GDF is used to represent input variables and give consistency to the use of the [PERSON] conditional probability theorem as a mechanism of attention and relationship or to weight input variables to the system. Moreover, the authors of [31] proposed the Probabilistic Fuzzy Naive Bayes classifier as a combination of probabilistic fuzzy systems and naive BN, which is also capable of simultaneously modeling both kinds of uncertainties. In this application, we can highlight that they did not develop a fuzzy system with a knowledge base; they only gave a linguistic interpretation to the GFD, and they used the Bayes theorem for the classifier's output, obtaining better results than a conventional naive Bayes classifier. Therefore, the work of [32] configured the supply chain, described the structure of a Bayesian network, and then determined a subsequent probability distribution for pending orders using a stochastic simulation based on Markov blankets. In this application, they do not develop a knowledge base as a system inference. They only determine the posteriori outputs with [PERSON] theorem. Furthermore, ref. [33] presented a robust methodology using the FMEA to detail the operational risks of the ballasting and de-ballasting process, reduced the subjectivity of RPN assessments with the evidence reasoning tool, and developed a Bayesian network to analyze the risk relationships of variables and quantify the power of the FMEA. In this paper, a Bayesian network was developed to conceptualize the risk system, unlike our proposal, which is conceptualized as a diffuse probabilistic system, where the Bayes theorem is used as a mechanism of attention to relate risk variables. In summary, FIS establishes a set of If-Then fuzzy rules that relate an antecedent structure with a consequent structure with a high interpretation of the system but do not contribute to the accuracy of the results. Generally, the antecedents of fuzzy rules are established objectively as being a combinatorial problem. On the contrary, at the stage of defining the consequence, there is the presence of subjectivity and imprecision because the fuzzy numbers of the structures do not need a linguistic interpretation. Nevertheless, the experts make their respective interpretations. However, the importance of each input variable is irrelevant to the development of FIS, and the weighting of input variables needs to be considered. Therefore, this research is focused on maintaining the high interpretation of the model based on the expert-driven approach and improving the accuracy of the results by using probabilistic numbers, developing a mechanism of attention to relating their values to each probabilistic rule through the conditional probability of the Bayes theorem as a data-driven approach. Each input variable is assigned a priori weighting based on the importance of the variable. The developed attention mechanism determines a posteriori weighting of the input variable based on linguistic data domain behavior. The significant contributions of this research are:  
1. Using the Bayes theorem, an attention mechanism was developed to relate the input variables. 2. Proposal for a normalization stage to determine probabilities a posteriori. 3. GDF is used to determine probabilistic values to input variables. 4. A Bayesian classifier is proposed at the implication stage. 5. Integration of stochastic uncertainty using probability density functions and linguistic uncertainty when categorizing functions based on expert interpretation. The rest of this manuscript is organized as follows. Section 2 describes the proposed method. Section 3 presents and discusses results of BLCS, and Section 4 concludes the proposed method.