{"title":"基于排序过程的实例选择算法","authors":"C. S. Pereira, George D. C. Cavalcanti","doi":"10.1109/IJCNN.2011.6033531","DOIUrl":null,"url":null,"abstract":"This paper presents an innovative instance selection method, called Instance Selection Algorithm based on a Ranking Procedure (ISAR), which is based on a ranking criterion. The ranking procedure aims to order the instances in the data set; better the instance higher the score associate to it. With the purpose of eliminating irrelevant instances, ISAR also uses a coverage strategy. Each instance delimits a hypersphere centered in it. The radius of each hypersphere is used as a normalization factor in the classification rule; bigger the radius smaller the distance. After a comparative study using real-world databases, the ISAR algorithm reached promising generalization performance and impressive reduction rates when compared with state of the art methods.","PeriodicalId":415833,"journal":{"name":"The 2011 International Joint Conference on Neural Networks","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Instance selection algorithm based on a Ranking Procedure\",\"authors\":\"C. S. Pereira, George D. C. Cavalcanti\",\"doi\":\"10.1109/IJCNN.2011.6033531\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an innovative instance selection method, called Instance Selection Algorithm based on a Ranking Procedure (ISAR), which is based on a ranking criterion. The ranking procedure aims to order the instances in the data set; better the instance higher the score associate to it. With the purpose of eliminating irrelevant instances, ISAR also uses a coverage strategy. Each instance delimits a hypersphere centered in it. The radius of each hypersphere is used as a normalization factor in the classification rule; bigger the radius smaller the distance. After a comparative study using real-world databases, the ISAR algorithm reached promising generalization performance and impressive reduction rates when compared with state of the art methods.\",\"PeriodicalId\":415833,\"journal\":{\"name\":\"The 2011 International Joint Conference on Neural Networks\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2011 International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2011.6033531\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2011 International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2011.6033531","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Instance selection algorithm based on a Ranking Procedure
This paper presents an innovative instance selection method, called Instance Selection Algorithm based on a Ranking Procedure (ISAR), which is based on a ranking criterion. The ranking procedure aims to order the instances in the data set; better the instance higher the score associate to it. With the purpose of eliminating irrelevant instances, ISAR also uses a coverage strategy. Each instance delimits a hypersphere centered in it. The radius of each hypersphere is used as a normalization factor in the classification rule; bigger the radius smaller the distance. After a comparative study using real-world databases, the ISAR algorithm reached promising generalization performance and impressive reduction rates when compared with state of the art methods.