{"title":"不平衡数据性能度量的理论分析","authors":"V. García, R. A. Mollineda, J. S. Sánchez","doi":"10.1109/ICPR.2010.156","DOIUrl":null,"url":null,"abstract":"This paper analyzes a generalization of a new metric to evaluate the classification performance in imbalanced domains, combining some estimate of the overall accuracy with a plain index about how dominant the class with the highest individual accuracy is. A theoretical analysis shows the merits of this metric when compared to other well-known measures.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"51","resultStr":"{\"title\":\"Theoretical Analysis of a Performance Measure for Imbalanced Data\",\"authors\":\"V. García, R. A. Mollineda, J. S. Sánchez\",\"doi\":\"10.1109/ICPR.2010.156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper analyzes a generalization of a new metric to evaluate the classification performance in imbalanced domains, combining some estimate of the overall accuracy with a plain index about how dominant the class with the highest individual accuracy is. A theoretical analysis shows the merits of this metric when compared to other well-known measures.\",\"PeriodicalId\":309591,\"journal\":{\"name\":\"2010 20th International Conference on Pattern Recognition\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"51\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 20th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2010.156\",\"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 20th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2010.156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Theoretical Analysis of a Performance Measure for Imbalanced Data
This paper analyzes a generalization of a new metric to evaluate the classification performance in imbalanced domains, combining some estimate of the overall accuracy with a plain index about how dominant the class with the highest individual accuracy is. A theoretical analysis shows the merits of this metric when compared to other well-known measures.