On the multistage Bayes classifier
Pattern Recognition
Fuzzy Sets and Systems
Arbitrarily Tight Upper and Lower Bounds on the Bayesian Probability of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian estimation in forest surveys when samples or prior information are fuzzy
Fuzzy Sets and Systems
Point estimation for the n sizes of random sample with one vague data
Fuzzy Sets and Systems
Lower Bounds for Bayes Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
On the methods of decision making under uncertainty with probability information
International Journal of Intelligent Systems
Two-stage binary classifier with fuzzy-valued loss function
Pattern Analysis & Applications
On the Mean Accuracy of Hierarchical Classifiers
IEEE Transactions on Computers
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Testing statistical hypotheses with vague data
Fuzzy Sets and Systems
Some propositions of information fusion for pattern recognition with context task
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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 intuitionistic fuzzy sets. A probability of misclassifications is derived for a classifier under the assumption that the features are class-conditionally statistically independent, and we have intuitionistic fuzzy information on object features instead of exact information. Additionally, a probability of the intuitionistic fuzzy event is represented by the real number. Numerical example concludes the work.