{"title":"The Method of Data Pre-processing in Grey Information Systems","authors":"S. X. Wu, Sifeng Liu, M. Q. Li","doi":"10.1109/ICARCV.2006.345360","DOIUrl":null,"url":null,"abstract":"In this paper, a method of data pre-processing in grey information systems was proposed to deal with grey information. The binning technique was introduced to smooth noisy data used for grey relative analysis. It constructed the function of grey relative coefficient for each null value and filled up the null value with the solution of the function. It also can be used to detect noisy data. This method is an application of grey system theory in data pre-processing. It has great significance in filling up null values and detecting noisy data in the \"poor\" information database","PeriodicalId":415827,"journal":{"name":"2006 9th International Conference on Control, Automation, Robotics and Vision","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 9th International Conference on Control, Automation, Robotics and Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCV.2006.345360","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, a method of data pre-processing in grey information systems was proposed to deal with grey information. The binning technique was introduced to smooth noisy data used for grey relative analysis. It constructed the function of grey relative coefficient for each null value and filled up the null value with the solution of the function. It also can be used to detect noisy data. This method is an application of grey system theory in data pre-processing. It has great significance in filling up null values and detecting noisy data in the "poor" information database