Improving accuracy of missing data imputation in data mining
نویسندگان
چکیده
منابع مشابه
Missing data imputation in multivariable time series data
Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...
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15 صفحه اولImproving Imputation Accuracy in Ordinal Data Using Classification
Tackling missing data is one of the fundamental data pre-processing steps. Data analysis and pattern extraction are affected due to the underlying differences between instances with and without missing data. This is a particular problem with ordinal data, where for example a sample of a population may have all failed to answer a specific question in a questionnaire. The existing methods such as...
متن کاملMultiple Imputation for Missing Data
Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard proc...
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ژورنال
عنوان ژورنال: Kurdistan Journal of Applied Research
سال: 2017
ISSN: 2411-7706,2411-7684
DOI: 10.24017/science.2017.3.30