Handling Numerical Missing Values Via Rough Sets
Автор: Elsayed Sallam, T. Medhat, A.Ghanem, M. E. Ali
Журнал: International Journal of Mathematical Sciences and Computing(IJMSC) @ijmsc
Статья в выпуске: 2 vol.3, 2017 года.
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Many existing industrial and research data sets contain missing values. Data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors, and incorrect measurements. It is usual to find missing data in most of the information sources used. Missing values usually appear as "NULL" values in the database or as empty cells in the spreadsheet table. Multiple ways have been used to deal with the problem of missing data. The proposed model presents rough set theory as a technique to deal with missing data. This model can handle the missing values for condition and decision attributes, the web application was developed to predict these values.
Rough sets, missing values, prediction, most common value
Короткий адрес: https://sciup.org/15014262
IDR: 15014262
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