MV.mostCommonVal: Replacing missing attribute values by the attribute mean or...

In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Replacing missing attribute values by the attribute mean or common values

Description

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.

Usage

MV.mostCommonVal(decision.table)

Arguments

decision.table

a "DecisionTable" class representing a decision table. See SF.asDecisionTable. Note: missing values are recognized as NA.

Value

A class "MissingValue". See MV.missingValueCompletion.

Author(s)

Lala Septem Riza

References

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

See Also

MV.missingValueCompletion

Examples

#############################################
## 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)

RoughSets documentation built on May 29, 2024, 7:34 a.m.