{"title":"数据孪生","authors":"Akhil Vakayil, V. R. Joseph","doi":"10.1002/sam.11574","DOIUrl":null,"url":null,"abstract":"In this work, we develop a method named Twinning for partitioning a dataset into statistically similar twin sets. Twinning is based on SPlit, a recently proposed model‐independent method for optimally splitting a dataset into training and testing sets. Twinning is orders of magnitude faster than the SPlit algorithm, which makes it applicable to Big Data problems such as data compression. Twinning can also be used for generating multiple splits of a given dataset to aid divide‐and‐conquer procedures and k‐fold cross validation.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"62 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Data Twinning\",\"authors\":\"Akhil Vakayil, V. R. Joseph\",\"doi\":\"10.1002/sam.11574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we develop a method named Twinning for partitioning a dataset into statistically similar twin sets. Twinning is based on SPlit, a recently proposed model‐independent method for optimally splitting a dataset into training and testing sets. Twinning is orders of magnitude faster than the SPlit algorithm, which makes it applicable to Big Data problems such as data compression. Twinning can also be used for generating multiple splits of a given dataset to aid divide‐and‐conquer procedures and k‐fold cross validation.\",\"PeriodicalId\":342679,\"journal\":{\"name\":\"Statistical Analysis and Data Mining: The ASA Data Science Journal\",\"volume\":\"62 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Analysis and Data Mining: The ASA Data Science Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/sam.11574\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Analysis and Data Mining: The ASA Data Science Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/sam.11574","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this work, we develop a method named Twinning for partitioning a dataset into statistically similar twin sets. Twinning is based on SPlit, a recently proposed model‐independent method for optimally splitting a dataset into training and testing sets. Twinning is orders of magnitude faster than the SPlit algorithm, which makes it applicable to Big Data problems such as data compression. Twinning can also be used for generating multiple splits of a given dataset to aid divide‐and‐conquer procedures and k‐fold cross validation.
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