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A more serious problem concerns numeric attributes.
The dataset has 2 numeric attributes, (A_1) and (A_2).
Six additional numeric attributes contain the corresponding probabilities.
The categorical variables were removed and only numeric attributes were used to do the imputation.
We assume that n numeric attributes exist in the dataset, which are denoted A1…A n.
Optimized for speed, it only sorts values for numeric attributes once.
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Finally, transforming the numeric attribute to the nominal attribute.
It is acknowledged that there is a loss of information when transforming a numeric attribute.
In the table, a numeric attribute with no more than two distinct values is regarded as a nominal attribute.
Because a small number of distinct values of a numeric attribute could be ambiguous with the nominal attribute.
In this paper, it uses the mean instead of missing values for numeric attribute and the most for nominal attributes.
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