Dylan C. Friel, Yunzhe Li, Benjamin Ellis, D. Jeske, Herbert K. H. Lee, P. Kass
{"title":"一个用于三个类的中性区域分类器,用于文本挖掘","authors":"Dylan C. Friel, Yunzhe Li, Benjamin Ellis, D. Jeske, Herbert K. H. Lee, P. Kass","doi":"10.1002/sam.11639","DOIUrl":null,"url":null,"abstract":"A classifier may be limited by its conditional misclassification rates more than its overall misclassification rate. In the case that one or more of the conditional misclassification rates are high, a neutral zone may be introduced to decrease and possibly balance the misclassification rates. In this paper, a neutral zone is incorporated into a three‐class classifier with its region determined by controlling conditional misclassification rates. The neutral zone classifier is illustrated with a text mining application that classifies written comments associated with student evaluations of teaching.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A neutral zone classifier for three classes with an application to text mining\",\"authors\":\"Dylan C. Friel, Yunzhe Li, Benjamin Ellis, D. Jeske, Herbert K. H. Lee, P. Kass\",\"doi\":\"10.1002/sam.11639\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A classifier may be limited by its conditional misclassification rates more than its overall misclassification rate. In the case that one or more of the conditional misclassification rates are high, a neutral zone may be introduced to decrease and possibly balance the misclassification rates. In this paper, a neutral zone is incorporated into a three‐class classifier with its region determined by controlling conditional misclassification rates. The neutral zone classifier is illustrated with a text mining application that classifies written comments associated with student evaluations of teaching.\",\"PeriodicalId\":342679,\"journal\":{\"name\":\"Statistical Analysis and Data Mining: The ASA Data Science Journal\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"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.11639\",\"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.11639","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A neutral zone classifier for three classes with an application to text mining
A classifier may be limited by its conditional misclassification rates more than its overall misclassification rate. In the case that one or more of the conditional misclassification rates are high, a neutral zone may be introduced to decrease and possibly balance the misclassification rates. In this paper, a neutral zone is incorporated into a three‐class classifier with its region determined by controlling conditional misclassification rates. The neutral zone classifier is illustrated with a text mining application that classifies written comments associated with student evaluations of teaching.