Description Usage Arguments Value Author(s) References See Also Examples
It is used for handling missing values by replacing the attribute mean or common values. If an attributes containing missing values is continuous/real, the method uses mean of the attribute instead of the most common value.
In order to generate a new decision table, we need to execute SF.applyDecTable
.
1 | MV.mostCommonVal(decision.table)
|
decision.table |
a |
A class "MissingValue"
. See MV.missingValueCompletion
.
Lala Septem Riza
J. Grzymala-Busse and W. Grzymala-Busse, "Handling Missing Attribute Values," in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds. New York : Springer, 2010, pp. 33-51
1 2 3 4 5 6 7 8 9 10 11 12 13 | #############################################
## Example: Replacing missing attribute values
## by the attribute mean/common values
#############################################
dt.ex1 <- data.frame(
c(100.2, 102.6, NA, 99.6, 99.8, 96.4, 96.6, NA),
c(NA, "yes", "no", "yes", NA, "yes", "no", "yes"),
c("no", "yes", "no", "yes", "yes", "no", "yes", NA),
c("yes", "yes", "no", "yes", "no", "no", "no", "yes"))
colnames(dt.ex1) <- c("Temp", "Headache", "Nausea", "Flu")
decision.table <- SF.asDecisionTable(dataset = dt.ex1, decision.attr = 4,
indx.nominal = c(2:4))
indx = MV.mostCommonVal(decision.table)
|
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