{"title":"集合交叉保角预测","authors":"Dorian Beganovic, E. Smirnov","doi":"10.1109/ICDMW.2018.00128","DOIUrl":null,"url":null,"abstract":"The cross-conformal prediction is an approach to confidence region prediction. It provides a trade-off between the validity and informational efficiency of the prediction regions from one hand and the computational complexity from another. In this paper we introduce a new cross-conformal approach based on ensembles. The new approach is more computationally efficient and provides gains in the validity and informational efficiency of the prediction regions. Hence, it is a good candidate for big data (analytics) when prediction regions with confidence values are required.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Ensemble Cross-Conformal Prediction\",\"authors\":\"Dorian Beganovic, E. Smirnov\",\"doi\":\"10.1109/ICDMW.2018.00128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The cross-conformal prediction is an approach to confidence region prediction. It provides a trade-off between the validity and informational efficiency of the prediction regions from one hand and the computational complexity from another. In this paper we introduce a new cross-conformal approach based on ensembles. The new approach is more computationally efficient and provides gains in the validity and informational efficiency of the prediction regions. Hence, it is a good candidate for big data (analytics) when prediction regions with confidence values are required.\",\"PeriodicalId\":259600,\"journal\":{\"name\":\"2018 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Data Mining Workshops (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2018.00128\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2018.00128","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The cross-conformal prediction is an approach to confidence region prediction. It provides a trade-off between the validity and informational efficiency of the prediction regions from one hand and the computational complexity from another. In this paper we introduce a new cross-conformal approach based on ensembles. The new approach is more computationally efficient and provides gains in the validity and informational efficiency of the prediction regions. Hence, it is a good candidate for big data (analytics) when prediction regions with confidence values are required.