Imprecise information in Bayes classifier

被引：0
|
作者：
Robert Burduk
机构：
[1] Wroclaw University of Technology,Department of Systems and Computer Networks
来源：
Pattern Analysis and Applications | 2012年 / 15卷
关键词：
Bayes decision rule; Imprecise observations; Probability of error;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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 or interval-valued 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 or interval-valued fuzzy information on object features instead of exact information. A probability of the intuitionistic or interval-valued fuzzy event is represented by the real number. Additionally, the received results are compared with the bound on the probability of error based on information energy. Numerical example concludes the work.
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页码：147 / 153
页数：6
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