Ansgar Steland, Ewaryst Rafajłowicz, Wojciech Rafajłowicz
{"title":"离散环境中的一般自适应阈值监测和不平衡类规则","authors":"Ansgar Steland, Ewaryst Rafajłowicz, Wojciech Rafajłowicz","doi":"10.1111/stan.12352","DOIUrl":null,"url":null,"abstract":"Having in mind applications in statistics and machine learning such as individualized care monitoring, or watermark detection in large language models, we consider the following general setting: When monitoring a sequence of observations, , there may be additional information, , on the environment which should be used to design the monitoring procedure. This additional information can be incorporated by applying threshold functions to the standardized measurements to adapt the detector to the environment. For the case of categorical data encoding of discrete‐valued environmental information we study several classes of level threshold functions including a proportional one which favors rare events among imbalanced classes. For the latter rule asymptotic theory is developed for independent and identically distributed and dependent learning samples including data from new discrete autoregressive moving average model (NDARMA) series and Hidden Markov Models. Further, we propose two‐stage designs which allow to distribute in a controlled way the budget over an a priori partition of the sample space of . The approach is illustrated by a real medical data set.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"General adapted‐threshold monitoring in discrete environments and rules for imbalanced classes\",\"authors\":\"Ansgar Steland, Ewaryst Rafajłowicz, Wojciech Rafajłowicz\",\"doi\":\"10.1111/stan.12352\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Having in mind applications in statistics and machine learning such as individualized care monitoring, or watermark detection in large language models, we consider the following general setting: When monitoring a sequence of observations, , there may be additional information, , on the environment which should be used to design the monitoring procedure. This additional information can be incorporated by applying threshold functions to the standardized measurements to adapt the detector to the environment. For the case of categorical data encoding of discrete‐valued environmental information we study several classes of level threshold functions including a proportional one which favors rare events among imbalanced classes. For the latter rule asymptotic theory is developed for independent and identically distributed and dependent learning samples including data from new discrete autoregressive moving average model (NDARMA) series and Hidden Markov Models. Further, we propose two‐stage designs which allow to distribute in a controlled way the budget over an a priori partition of the sample space of . The approach is illustrated by a real medical data set.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1111/stan.12352\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1111/stan.12352","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
General adapted‐threshold monitoring in discrete environments and rules for imbalanced classes
Having in mind applications in statistics and machine learning such as individualized care monitoring, or watermark detection in large language models, we consider the following general setting: When monitoring a sequence of observations, , there may be additional information, , on the environment which should be used to design the monitoring procedure. This additional information can be incorporated by applying threshold functions to the standardized measurements to adapt the detector to the environment. For the case of categorical data encoding of discrete‐valued environmental information we study several classes of level threshold functions including a proportional one which favors rare events among imbalanced classes. For the latter rule asymptotic theory is developed for independent and identically distributed and dependent learning samples including data from new discrete autoregressive moving average model (NDARMA) series and Hidden Markov Models. Further, we propose two‐stage designs which allow to distribute in a controlled way the budget over an a priori partition of the sample space of . The approach is illustrated by a real medical data set.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.