RoughSets (version 1.2-1)

MV.missingValueCompletion: Wrapper function of missing value completion

Description

It is a wrapper function for missing value completion.

Usage

MV.missingValueCompletion(decision.table, type.method = "deletionCases")

Arguments

decision.table

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

type.method

one of the following methods:

• "deletionCases": SeeMV.deletionCases.
• "mostCommonValResConcept": See

Value

• A class "MissingValue" which contains
  • val.NA: a matrix containing indices of missing value (i.e., unknown values) positions and their values.
  • type.method: a string showing the type of used method. In this case, it is"deleteCases".

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.deletionCases, MV.mostCommonValResConcept, MV.mostCommonVal, MV.globalClosestFit, and MV.conceptClosestFit.

Examples

#############################################
## Example :
#############################################
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.missingValueCompletion(decision.table, type.method = "deletionCases")

## generate new decision table
new.decTable <- SF.applyDecTable(decision.table, indx)