Supervised discretization based on the local discernibility heuristic

Description

It is a function used for computing locally semi-optimal cuts using the local discernibility heuristic.

Usage

1
2
D.local.discernibility.heuristic.RST(decision.table, maxNOfCuts = 2,
  cutCandidatesList = NULL, discFunction = local.discernibility)

Arguments

decision.table

an object inheriting from the "DecisionTable" class, which represents a decision system. See SF.asDecisionTable. It should be noted that for this particular method all conditional attributes must be numeric.

maxNOfCuts

a positive integer indicating the maximum number of allowed cuts on a single attribute.

cutCandidatesList

an optional list containing candidates for optimal cut values. By default the candidating cuts are determined automatically.

discFunction

a function used for computation of cuts. Currently only one implementation of the local discernibility heuristic is available (the default). However, this parameter can be used to integrate custom implementations of discretization functions with the RoughSets package.

Details

A local (univariate) version of the algorithm described in (Nguyen, 2001) and (Bazan et al., 2000).

The output of this function is an object of a class "Discretization" which contains cut values. The function SF.applyDecTable has to be used in order to generate the new (discretized) decision table.

Value

An object of a class "Discretization" which stores cuts for each conditional attribute. See D.discretization.RST.

Author(s)

Andrzej Janusz

References

S. H. Nguyen, "On Efficient Handling of Continuous Attributes in Large Data Bases", Fundamenta Informaticae, vol. 48, p. 61 - 81 (2001).

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

D.discretize.quantiles.RST, D.discretize.equal.intervals.RST, D.global.discernibility.heuristic.RST and SF.applyDecTable. A wrapper function for all available discretization methods: D.discretization.RST

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
#################################################################
## Example: Determine cut values and generate new decision table
#################################################################
data(RoughSetData)
wine.data <- RoughSetData$wine.dt
cut.values <- D.local.discernibility.heuristic.RST(wine.data)

## generate a new decision table:
wine.discretized <- SF.applyDecTable(wine.data, cut.values)
dim(wine.discretized)
lapply(wine.discretized, unique)

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