{"title":"Mathematical Analysis on Weight Vectors in Text Classification","authors":"Fengxi Song, Qinglong Chen, Zhongwei Guo, Xiumei Gao","doi":"10.1109/GCIS.2012.14","DOIUrl":null,"url":null,"abstract":"By means of rigid mathematical deductions we prove that weight vectors cannot promote the performance of the optimal classifier, i.e. the Bayesian classifier in terms of the error, F-one score, or breakeven point. The conclusion is important in that people used to promote the performance of a classifier by trying various weight vectors in text classification.","PeriodicalId":337629,"journal":{"name":"2012 Third Global Congress on Intelligent Systems","volume":"345 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third Global Congress on Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCIS.2012.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
By means of rigid mathematical deductions we prove that weight vectors cannot promote the performance of the optimal classifier, i.e. the Bayesian classifier in terms of the error, F-one score, or breakeven point. The conclusion is important in that people used to promote the performance of a classifier by trying various weight vectors in text classification.