A. Lipitakis, G. Antzoulatos, S. Kotsiantis, M. Vrahatis
{"title":"整合全球和地方促进","authors":"A. Lipitakis, G. Antzoulatos, S. Kotsiantis, M. Vrahatis","doi":"10.1109/IISA.2015.7388123","DOIUrl":null,"url":null,"abstract":"Several data analysis problems require investigations of relationships between attributes in related heterogeneous databases, where different prediction models can be more appropriate for different regions. A new technique of integrating global and local boosting is proposed. A comparison with other well known and widely used combining methods on standard benchmark datasets has shown that the proposed technique leads to more accurate results.","PeriodicalId":433872,"journal":{"name":"2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating global and local boosting\",\"authors\":\"A. Lipitakis, G. Antzoulatos, S. Kotsiantis, M. Vrahatis\",\"doi\":\"10.1109/IISA.2015.7388123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Several data analysis problems require investigations of relationships between attributes in related heterogeneous databases, where different prediction models can be more appropriate for different regions. A new technique of integrating global and local boosting is proposed. A comparison with other well known and widely used combining methods on standard benchmark datasets has shown that the proposed technique leads to more accurate results.\",\"PeriodicalId\":433872,\"journal\":{\"name\":\"2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IISA.2015.7388123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IISA.2015.7388123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Several data analysis problems require investigations of relationships between attributes in related heterogeneous databases, where different prediction models can be more appropriate for different regions. A new technique of integrating global and local boosting is proposed. A comparison with other well known and widely used combining methods on standard benchmark datasets has shown that the proposed technique leads to more accurate results.
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