Abstract
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, we consider the global optimal hierarchical classifier.
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Burduk, R. (2009). Probability Error in Global Optimal Hierarchical Classifier with Intuitionistic Fuzzy Observations. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_64
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DOI: https://doi.org/10.1007/978-3-642-02319-4_64
Publisher Name: Springer, Berlin, Heidelberg
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