{"title":"ClaDia:用于疾病诊断的模糊分类系统","authors":"D. Walter, C. K. Mohan","doi":"10.1109/CEC.2000.870821","DOIUrl":null,"url":null,"abstract":"The paper describes ClaDia, a learning classifier system applied to the Wisconsin breast cancer data set, using a fuzzy representation of the rules, a median based fuzzy combination rule, and separate subpopulations for each class. The system achieves a classification rate of over 90%, for many sets of system parameter values.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"49","resultStr":"{\"title\":\"ClaDia: a fuzzy classifier system for disease diagnosis\",\"authors\":\"D. Walter, C. K. Mohan\",\"doi\":\"10.1109/CEC.2000.870821\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes ClaDia, a learning classifier system applied to the Wisconsin breast cancer data set, using a fuzzy representation of the rules, a median based fuzzy combination rule, and separate subpopulations for each class. The system achieves a classification rate of over 90%, for many sets of system parameter values.\",\"PeriodicalId\":218136,\"journal\":{\"name\":\"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"49\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2000.870821\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2000.870821","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ClaDia: a fuzzy classifier system for disease diagnosis
The paper describes ClaDia, a learning classifier system applied to the Wisconsin breast cancer data set, using a fuzzy representation of the rules, a median based fuzzy combination rule, and separate subpopulations for each class. The system achieves a classification rate of over 90%, for many sets of system parameter values.
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