{"title":"Fuzzy Data in Local Optimal Strategy of Multistage Recognition","authors":"R. Burduk","doi":"10.1109/CISIM.2007.33","DOIUrl":null,"url":null,"abstract":"The paper deals with the recognition task with a full probabilistic information. In this problem of recognition the Bayesian statistic is applied and multistage recognition is considered. The information on objects features is fuzzy-valued. The decision rules minimize the mean risk, that is the mean value of the zero-one loss function. The probability of misclassification for local optimal strategy and the difference between probability of misclassification for the fuzzy-valued and crisp data are presented. Simple example conclude the work and influence of the width of the fuzzy number on this difference is discussed.","PeriodicalId":350490,"journal":{"name":"6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISIM.2007.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The paper deals with the recognition task with a full probabilistic information. In this problem of recognition the Bayesian statistic is applied and multistage recognition is considered. The information on objects features is fuzzy-valued. The decision rules minimize the mean risk, that is the mean value of the zero-one loss function. The probability of misclassification for local optimal strategy and the difference between probability of misclassification for the fuzzy-valued and crisp data are presented. Simple example conclude the work and influence of the width of the fuzzy number on this difference is discussed.
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