{"title":"用于成本敏感数据挖掘的完全分布式框架","authors":"Wei Fan, Haixun Wang, Philip S. Yu, S. Stolfo","doi":"10.1109/ICDCS.2002.1022284","DOIUrl":null,"url":null,"abstract":"We propose a fully distributed system (as compared to centralized and partially distributed systems) for cost-sensitive data mining. Experimental results have shown that this approach achieves higher accuracy than both the centralized and partially distributed learning methods, however, it incurs much less training time, neither communication nor computation overhead.","PeriodicalId":186210,"journal":{"name":"Proceedings 22nd International Conference on Distributed Computing Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A fully distributed framework for cost-sensitive data mining\",\"authors\":\"Wei Fan, Haixun Wang, Philip S. Yu, S. Stolfo\",\"doi\":\"10.1109/ICDCS.2002.1022284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a fully distributed system (as compared to centralized and partially distributed systems) for cost-sensitive data mining. Experimental results have shown that this approach achieves higher accuracy than both the centralized and partially distributed learning methods, however, it incurs much less training time, neither communication nor computation overhead.\",\"PeriodicalId\":186210,\"journal\":{\"name\":\"Proceedings 22nd International Conference on Distributed Computing Systems\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 22nd International Conference on Distributed Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDCS.2002.1022284\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 22nd International Conference on Distributed Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS.2002.1022284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A fully distributed framework for cost-sensitive data mining
We propose a fully distributed system (as compared to centralized and partially distributed systems) for cost-sensitive data mining. Experimental results have shown that this approach achieves higher accuracy than both the centralized and partially distributed learning methods, however, it incurs much less training time, neither communication nor computation overhead.
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