MV.mostCommonValResConcept: The most common value or mean of an attribute restricted to a concept

Description

It is used for handling missing values by assigning the most common value of an attribute restricted to a concept. 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.mostCommonValResConcept(decision.table)

Value

A class "MissingValue". See MV.missingValueCompletion.

Arguments

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

Author

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

Examples

Run this code

#############################################
## Example: The most common value
#############################################
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.mostCommonValResConcept(decision.table)

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