RoughSets (version 1.3-8)

BC.positive.reg.RST: Computation of a positive region

Description

This function implements a fundamental part of RST: computation of a positive region and the degree of dependency. This function can be used as a basic building block for development of other RST-based methods. A more detailed explanation of this notion can be found in Introduction-RoughSets.

Usage

BC.positive.reg.RST(decision.table, roughset)

Value

An object of a class "PositiveRegion" which is a list with the following components:

• positive.reg: an integer vector containing indices of data instances belonging to the positive region,

• degree.dependency: a numeric value giving the degree of dependency,

• type.model: a varacter vector identifying the utilized model. In this case, it is "RST" which means the rough set theory.

Arguments

decision.table

an object inheriting from the "DecisionTable" class, which represents a decision system. See SF.asDecisionTable.

roughset

an object inheriting from the "LowerUpperApproximation" class, which represents lower and upper approximations of decision classes in the data. Such objects are typically produced by calling the BC.LU.approximation.RST function.

Author

Andrzej Janusz

References

Z. Pawlak, "Rough Sets", International Journal of Computer and Information Sciences, vol. 11, no. 5, p. 341 - 356 (1982).

See Also

BC.IND.relation.RST, BC.LU.approximation.RST, BC.LU.approximation.FRST

Examples

########################################################
data(RoughSetData)
hiring.data <- RoughSetData$hiring.dt

## We select a single attribute for computation of indiscernibility classes:
A <- c(2)

## compute the indiscernibility classes:
IND.A <- BC.IND.relation.RST(hiring.data, feature.set = A)

## compute the lower and upper approximation:
roughset <- BC.LU.approximation.RST(hiring.data, IND.A)

## get the positive region:
pos.region = BC.positive.reg.RST(hiring.data, roughset)
pos.region