Measuring uncertainty in low frequency data sets by fuzzy imprecision & probabilistic inferencing

Measurement of uncertainty in low frequency data sets by making a marriage between fuzzy logic and probabilistic inferencing is the focus of this research. We investigated the nature of vague and rarefied data with a view to develop an approximate reasoning algorithm for making decisions on the given data. A clear distinction between vague data and low frequency data was established. We have reviewed some of the drawbacks existed in the conventional definitions of fuzzy set theoretic operations. Finally, a probabilistic inferencing scheme was proposed in which the probability intervals are formed based on the values obtained by evaluating fuzzy expressions