{"title":"Calculating different weights in feature values in logistic regression","authors":"Chang-Hwan Lee","doi":"10.1145/3018009.3018017","DOIUrl":null,"url":null,"abstract":"In traditional logistic regression model, every value of feature has the same weight. In this paper, we propose a new weighting method for logistic regression, which assigns a different weight to each feature value. A gradient approach is used to calculate the optimal weights of feature values.","PeriodicalId":189252,"journal":{"name":"Proceedings of the 2nd International Conference on Communication and Information Processing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd International Conference on Communication and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3018009.3018017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In traditional logistic regression model, every value of feature has the same weight. In this paper, we propose a new weighting method for logistic regression, which assigns a different weight to each feature value. A gradient approach is used to calculate the optimal weights of feature values.