A generalized approach to construct node probability table for Bayesian belief network using fuzzy logic

The cause-effect relationship has tremendous role in interpreting the engineering and scientific problems which basically deals with the identifying potential causes of problem. Bayesian belief networks (BBN) also referred as Bayesian casual probabilistic network used widely to deal with probabilistic events to elucidate the complications having uncertainty. A major challenge in BBN is to construct a node probability table (NPT), which grows exponentially with the rising number of variables. Various approaches exist for NPT construction, including expert elicitation, data analysis, survey and weighted functions, noisy-OR, noisy-MAX, recursive noisy-OR (ROR), extended recursive noisy-OR, and ranked nodes. However, these methods are problem-specific and lacking behind a generalized approach applicable to all problem types. To address this issue, this paper proposes a generalized universal approach for constructing the NPT using fuzzy logic. The suggested strategy has been validated by applying it to a BBN prototype for software design and development. The proposed strategy has been evaluated with best-case and worst-case software metrics.