Computing one reduct from a discernibility matrix

Description

It is a function for computing one reduct from a discernibility matrix - it can use the greedy heuristic or a randomized (Monte Carlo) search.

Usage

1
FS.one.reduct.computation(discernibilityMatrix, greedy = TRUE, power = 1)

Arguments

discernibilityMatrix

a "DiscernibilityMatrix" class representing the discernibility matrix of RST and FRST.

greedy

a boolean value indicating whether the greedy heuristic or a randomized search should be used in computations.

power

a numeric representing a parameter of the randomized search heuristic.

Value

A class "ReductSet".

Author(s)

Andrzej Janusz

References

Jan G. Bazan, Hung Son Nguyen, Sinh Hoa Nguyen, Piotr Synak, and Jakub Wroblewski, "Rough Set Algorithms in Classification Problem", Chapter 2 In: L. Polkowski, S. Tsumoto and T.Y. Lin (eds.): Rough Set Methods and Applications Physica-Verlag, Heidelberg, New York, p. 49 - 88 ( 2000).

See Also

BC.discernibility.mat.RST and BC.discernibility.mat.FRST.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
########################################################
## Example 1: Generate one reducts and
##            a new decision table using RST
########################################################
data(RoughSetData)
decision.table <- RoughSetData$hiring.dt

## build the decision-relation discernibility matrix
res.1 <- BC.discernibility.mat.RST(decision.table, range.object = NULL)

## generate all reducts
reduct <- FS.one.reduct.computation(res.1)

## generate new decision table
new.decTable <- SF.applyDecTable(decision.table, reduct, control = list(indx.reduct = 1))

##############################################################
## Example 2: Generate one reducts and
##            a new decision table using FRST
##############################################################
data(RoughSetData)
decision.table <- RoughSetData$hiring.dt

## build the decision-relation discernibility matrix
control <- list(type.relation = c("crisp"),
                type.aggregation = c("crisp"),
                t.implicator = "lukasiewicz", type.LU = "implicator.tnorm")
res.2 <- BC.discernibility.mat.FRST(decision.table, type.discernibility = "standard.red",
                                    control = control)

## generate single reduct
reduct <- FS.one.reduct.computation(res.2)

## generate new decision table
new.decTable <- SF.applyDecTable(decision.table, reduct, control = list(indx.reduct = 1))

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.