{"title":"专一性的语言学方法","authors":"C. Butler, J. Yen","doi":"10.1109/NAFIPS.1999.781707","DOIUrl":null,"url":null,"abstract":"Understanding the level of uncertainty associated with a prediction is valuable in determining its utility in decision making. One measure of information is R.R. Yager's (1982) notion of specificity. Yager views specificity as the degree to which a possibility distribution points to a single element in the universe of discourse (U). Specificity in relation to U may complicate its utility in the optimization of fuzzy models in their linguistic space. An increase in granularity is useful to measure the amount of information contained in a possibility distribution in relation to fuzzy sets as opposed to U. This abstracted view of specificity motivates the need for a more generalized version of specificity, denoted linguistic specificity (Sp/sub L/), where alternatives are measured in relation to the linguistic terms. Such a generalization is useful in support of automating decisions in a fuzzy domain. Results of the linguistic specificity measure are illustrated using an automobile fuel consumption example.","PeriodicalId":335957,"journal":{"name":"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A linguistic approach to specificity\",\"authors\":\"C. Butler, J. Yen\",\"doi\":\"10.1109/NAFIPS.1999.781707\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Understanding the level of uncertainty associated with a prediction is valuable in determining its utility in decision making. One measure of information is R.R. Yager's (1982) notion of specificity. Yager views specificity as the degree to which a possibility distribution points to a single element in the universe of discourse (U). Specificity in relation to U may complicate its utility in the optimization of fuzzy models in their linguistic space. An increase in granularity is useful to measure the amount of information contained in a possibility distribution in relation to fuzzy sets as opposed to U. This abstracted view of specificity motivates the need for a more generalized version of specificity, denoted linguistic specificity (Sp/sub L/), where alternatives are measured in relation to the linguistic terms. Such a generalization is useful in support of automating decisions in a fuzzy domain. Results of the linguistic specificity measure are illustrated using an automobile fuel consumption example.\",\"PeriodicalId\":335957,\"journal\":{\"name\":\"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.1999.781707\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.1999.781707","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Understanding the level of uncertainty associated with a prediction is valuable in determining its utility in decision making. One measure of information is R.R. Yager's (1982) notion of specificity. Yager views specificity as the degree to which a possibility distribution points to a single element in the universe of discourse (U). Specificity in relation to U may complicate its utility in the optimization of fuzzy models in their linguistic space. An increase in granularity is useful to measure the amount of information contained in a possibility distribution in relation to fuzzy sets as opposed to U. This abstracted view of specificity motivates the need for a more generalized version of specificity, denoted linguistic specificity (Sp/sub L/), where alternatives are measured in relation to the linguistic terms. Such a generalization is useful in support of automating decisions in a fuzzy domain. Results of the linguistic specificity measure are illustrated using an automobile fuel consumption example.