BC.discernibility.positive.mat.RST: Computation of a positive decision-relative discernibility...
In janusza/RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/FeatureSelection.Experimental.R

This function implements a positive decision-relative discernibility matrix. This notion was proposed by (Sikora et al.) as a middle-step in many RST algorithms for computaion of reducts, discretization and rule induction in a case when the discernibility of objects from the positive class by positive attribute values is more desirable than by the negative ones. The implementation currently works only for binary decision system (all attributes, including the decision must be binary and the positive value is marked by "1").

BC.discernibility.positive.mat.RST(
  decision.table,
  return.matrix = FALSE,
  attach.data = FALSE
)

`decision.table`	an object inheriting from the `DecisionTable` class, which represents a decision system. See `SF.asDecisionTable`.
`return.matrix`	a logical value determining whether the discernibility matrix should be retunred in the output. If it is set to FALSE (the default) only a list containing unique clauses from the CNF representation of the discernibility function is returned.
`attach.data`	a logical indicating whether the original decision table should be attached as an additional element of the resulting list named as `dec.table`.

An object of a class DiscernibilityMatrix which has the following components:

disc.mat: the decision-relative discernibility matrix which for pairs of objects from different decision classes stores names of attributes which can be used to discern between them. Only pairs of objects from different decision classes are considered. For other pairs the disc.mat contains NA values. Moreover, since the classical discernibility matrix is symmetric only the pairs from the lower triangular part are considered.
disc.list: a list containing unique clauses from the CNF representation of the discernibility function,
dec.table: an object of a class DecisionTable, which was used to compute the discernibility matrix,
discernibility.type: a type of discernibility relation used in the computations,
type.model: a character vector identifying the type of model which was used. In this case, it is "RST" which means the rough set theory.

Andrzej Janusz and Dominik Slezak

TO BE ADDED

BC.IND.relation.RST, BC.LU.approximation.RST, BC.LU.approximation.FRST and BC.discernibility.mat.FRST

###############################################################################
## Example 1: Constructing the positive decision-relative discernibility matrix
###############################################################################
data(RoughSetData)
binary.dt <- RoughSetData$binary.dt

## building the decision-relation discernibility matrix
disc.matrix <- BC.discernibility.positive.mat.RST(binary.dt, return.matrix = TRUE)
disc.matrix$disc.mat

## compute all classical reducts
classic.reducts <- FS.all.reducts.computation(BC.discernibility.mat.RST(binary.dt))
head(classic.reducts$decision.reduct)
cat("A total number of reducts found: ", 
    length(classic.reducts$decision.reduct), "\n", sep = "")
classic.reducts$core

## compute all positive reducts
positive.reducts <- FS.all.reducts.computation(disc.matrix)
head(positive.reducts$decision.reduct)
cat("A total number of positive reducts found: ", 
    length(positive.reducts$decision.reduct), "\n", sep = "")
print("The core:")
positive.reducts$core