Abstract
The paper considers the mixture of randomness and fuzziness in Bayes multistage classifier. Assuming that both the tree structure and the feature used at each non-terminal node have been specified, we present the probability of error. This model of classification is based on the fuzzy observations, the randomness of classes and the Bayes rule. The obtained error for fuzzy observations is compared with the case when observation are not fuzzy as a difference of errors. Additionally, the obtained results are compared with the bound on the probability of error based on information energy of fuzzy events.
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Burduk, R. (2010). Randomness and Fuzziness in Bayes Multistage Classifier. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_65
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DOI: https://doi.org/10.1007/978-3-642-13769-3_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13768-6
Online ISBN: 978-3-642-13769-3
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