Exact classification error in bayes classifier with fuzzy observations

The paper considers the problem of classification error in pattern recognition. This model of classification is primarily based on the Bayes rule and secondarily on the notion of fuzzy numbers. A probability of misclassifications is derived for a classifier under the assumption that the features are class-conditionally statistically independent, and we have fuzzy information on object features instead of exact information. Numerical example of this difference concludes the work.