{"title":"一种改进的不完全数据特征关系","authors":"Yin Xu-ri","doi":"10.1109/KAMW.2008.4810442","DOIUrl":null,"url":null,"abstract":"In the classical rough set theory, the use of the indiscernibility relation which is used in the complete information systems may be too rigid in some real situations. In order to process incomplete data, the indiscernibility relation needs to be extended. In this paper, after discussing the basic concepts and current research on the characteristic relation under incomplete data, a modified characteristic relation that is dependent on the number of missing values with respect to the number of the whole defined attributes for each object is introduced; the lower and upper approximation defined on this relation are proposed as well. Furthermore, we present some properties of this modified characteristic relation. The experiments show that this relation works effectively in incomplete information and generates object classification reasonably. This electronic document is a \"live\" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.","PeriodicalId":375613,"journal":{"name":"2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop","volume":"160 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Modified Characteristic Relation for Incomplete Data\",\"authors\":\"Yin Xu-ri\",\"doi\":\"10.1109/KAMW.2008.4810442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the classical rough set theory, the use of the indiscernibility relation which is used in the complete information systems may be too rigid in some real situations. In order to process incomplete data, the indiscernibility relation needs to be extended. In this paper, after discussing the basic concepts and current research on the characteristic relation under incomplete data, a modified characteristic relation that is dependent on the number of missing values with respect to the number of the whole defined attributes for each object is introduced; the lower and upper approximation defined on this relation are proposed as well. Furthermore, we present some properties of this modified characteristic relation. The experiments show that this relation works effectively in incomplete information and generates object classification reasonably. This electronic document is a \\\"live\\\" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.\",\"PeriodicalId\":375613,\"journal\":{\"name\":\"2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop\",\"volume\":\"160 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/KAMW.2008.4810442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KAMW.2008.4810442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Modified Characteristic Relation for Incomplete Data
In the classical rough set theory, the use of the indiscernibility relation which is used in the complete information systems may be too rigid in some real situations. In order to process incomplete data, the indiscernibility relation needs to be extended. In this paper, after discussing the basic concepts and current research on the characteristic relation under incomplete data, a modified characteristic relation that is dependent on the number of missing values with respect to the number of the whole defined attributes for each object is introduced; the lower and upper approximation defined on this relation are proposed as well. Furthermore, we present some properties of this modified characteristic relation. The experiments show that this relation works effectively in incomplete information and generates object classification reasonably. This electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.