{"title":"一种新的模糊分类隶属函数定义","authors":"Nur UYLAŞ SATI","doi":"10.53433/yyufbed.1239769","DOIUrl":null,"url":null,"abstract":"In this paper, a novel membership function is defined for fuzzy sets by using supervised learning approach. Firstly, in a supervised learning approach, training dataset is separated with the previously defined polyhedral conic functions. Then obtained polyhedral conic functions are used for defining a new membership function. After that by using this function a new fuzzy classification algorithm is defined to classify fuzzy sets that have the similar structure. The algorithm with all suggested methods is implemented on real-world datasets and the performance values are compared with the state of art classification algorithms.","PeriodicalId":386555,"journal":{"name":"Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Membership Function Definition for Fuzzy Classification\",\"authors\":\"Nur UYLAŞ SATI\",\"doi\":\"10.53433/yyufbed.1239769\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a novel membership function is defined for fuzzy sets by using supervised learning approach. Firstly, in a supervised learning approach, training dataset is separated with the previously defined polyhedral conic functions. Then obtained polyhedral conic functions are used for defining a new membership function. After that by using this function a new fuzzy classification algorithm is defined to classify fuzzy sets that have the similar structure. The algorithm with all suggested methods is implemented on real-world datasets and the performance values are compared with the state of art classification algorithms.\",\"PeriodicalId\":386555,\"journal\":{\"name\":\"Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53433/yyufbed.1239769\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53433/yyufbed.1239769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Membership Function Definition for Fuzzy Classification
In this paper, a novel membership function is defined for fuzzy sets by using supervised learning approach. Firstly, in a supervised learning approach, training dataset is separated with the previously defined polyhedral conic functions. Then obtained polyhedral conic functions are used for defining a new membership function. After that by using this function a new fuzzy classification algorithm is defined to classify fuzzy sets that have the similar structure. The algorithm with all suggested methods is implemented on real-world datasets and the performance values are compared with the state of art classification algorithms.