{"title":"生物医学数据分类的最佳模糊逻辑方法","authors":"O. Polat","doi":"10.1109/BIYOMUT.2010.5479731","DOIUrl":null,"url":null,"abstract":"In this study, a fuzzy logic technique for classification of different biomedical dataset is optimized for improving performance. Threshold acceptance algorithm is used for optimization process. The proposed method is tested on heart data set and simulation results show that the optimized fuzzy logic approach provides higher classification accuracy compare with that of unoptimized fuzzy logic structure.","PeriodicalId":180275,"journal":{"name":"2010 15th National Biomedical Engineering Meeting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The optimum fuzzy logic approach for biomedical data classification\",\"authors\":\"O. Polat\",\"doi\":\"10.1109/BIYOMUT.2010.5479731\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, a fuzzy logic technique for classification of different biomedical dataset is optimized for improving performance. Threshold acceptance algorithm is used for optimization process. The proposed method is tested on heart data set and simulation results show that the optimized fuzzy logic approach provides higher classification accuracy compare with that of unoptimized fuzzy logic structure.\",\"PeriodicalId\":180275,\"journal\":{\"name\":\"2010 15th National Biomedical Engineering Meeting\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 15th National Biomedical Engineering Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIYOMUT.2010.5479731\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 15th National Biomedical Engineering Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIYOMUT.2010.5479731","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The optimum fuzzy logic approach for biomedical data classification
In this study, a fuzzy logic technique for classification of different biomedical dataset is optimized for improving performance. Threshold acceptance algorithm is used for optimization process. The proposed method is tested on heart data set and simulation results show that the optimized fuzzy logic approach provides higher classification accuracy compare with that of unoptimized fuzzy logic structure.
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