{"title":"一种改进的模糊多类双支持向量机","authors":"H. Ju, Ling Jing","doi":"10.1109/ICSAI48974.2019.9010402","DOIUrl":null,"url":null,"abstract":"Multi-class twin support vector machine (MTSVM) is a multi-class classification algorithm. It has extensive application in the multi-class classification problems. In order to improve the classification performance of MTSVM, an improved fuzzy multiclass twin support vector machine (IF-MTSVM) is proposed. Then the solution to IF-MTSVM is derived. Experiments on ordinary UCI datasets and datasets with 5% noises added show that this new method has certain advantages over other fuzzy multi-class twin support vector machines.","PeriodicalId":270809,"journal":{"name":"2019 6th International Conference on Systems and Informatics (ICSAI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Improved Fuzzy Multi-class Twin Support Vector Machine\",\"authors\":\"H. Ju, Ling Jing\",\"doi\":\"10.1109/ICSAI48974.2019.9010402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-class twin support vector machine (MTSVM) is a multi-class classification algorithm. It has extensive application in the multi-class classification problems. In order to improve the classification performance of MTSVM, an improved fuzzy multiclass twin support vector machine (IF-MTSVM) is proposed. Then the solution to IF-MTSVM is derived. Experiments on ordinary UCI datasets and datasets with 5% noises added show that this new method has certain advantages over other fuzzy multi-class twin support vector machines.\",\"PeriodicalId\":270809,\"journal\":{\"name\":\"2019 6th International Conference on Systems and Informatics (ICSAI)\",\"volume\":\"54 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 6th International Conference on Systems and Informatics (ICSAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSAI48974.2019.9010402\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 6th International Conference on Systems and Informatics (ICSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI48974.2019.9010402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved Fuzzy Multi-class Twin Support Vector Machine
Multi-class twin support vector machine (MTSVM) is a multi-class classification algorithm. It has extensive application in the multi-class classification problems. In order to improve the classification performance of MTSVM, an improved fuzzy multiclass twin support vector machine (IF-MTSVM) is proposed. Then the solution to IF-MTSVM is derived. Experiments on ordinary UCI datasets and datasets with 5% noises added show that this new method has certain advantages over other fuzzy multi-class twin support vector machines.