R: The greedy heuristic method for determining superreduct based...

FS.greedy.heuristic.superreduct.RST {RoughSets} | R Documentation

## The greedy heuristic method for determining superreduct based on RST

### Description

It is used to get a feature subset (superreduct) based on the greedy heuristic algorithm employing some quality measurements. Regarding the quality measurements, the detailed description can be seen in `FS.greedy.heuristic.reduct.RST`.

### Usage

    FS.greedy.heuristic.superreduct.RST(decision.table,
      attrDescriptions = attr(decision.table, "desc.attrs"),
      decisionIdx = attr(decision.table, "decision.attr"),
      qualityF = X.gini, nAttrs = NULL, inconsistentDecisionTable = NULL)

### Arguments

`decision.table` | an object of a `"DecisionTable"` class representing a decision table. See `SF.asDecisionTable`.
`attrDescriptions` | a list containing possible values of attributes (columns) in `decision.table`. It usually corresponds to `attr(decision.table, "desc.attrs")`.
`decisionIdx` | a integer value representing an index of decision attribute.
`qualityF` | a function for calculating a quality of an attribute subset. See `FS.greedy.heuristic.reduct.RST`.
`nAttrs` | an integer between 1 and the number of conditional attributes. It indicates the attribute sample size for the Monte Carlo selection of candidating attributes. If set to `NULL` (default) all attributes are used and the algorithm changes to a standard greedy method for computation of decision reducts.
`inconsistentDecisionTable` | logical indicating whether the decision table is suspected to be inconsistent or `NULL` (the default) which indicated that a test should be made to determine the data consistency.

### Value

A class `"FeatureSubset"` that contains the following components:

- `reduct`: a list representing a single reduct. In this case, it could be a superreduct or just a subset of features.

- `type.method`: a string representing the type of method which is `"greedy.heuristic.superreduct"`.

- `type.task`: a string showing the type of task which is `"feature selection"`.

- `model`: a string representing the type of model. In this case, it is `"RST"` which means rough set theory.

### Author(s)

Andrzej Janusz

### References

Andrzej Janusz and Dominik Slezak. "Rough Set Methods for Attribute Clustering and Selection". Applied Artificial Intelligence, 28(3):220–242, 2014.

A. Janusz and S. Stawicki, "Applications of Approximate Reducts to the Feature Selection Problem", Proceedings of International Conference on Rough Sets and Knowledge Technology (RSKT), vol. 6954, p. 45 - 50 (2011).

D. Ślęzak, "Approximate Entropy Reducts", Fundamenta Informaticae, vol. 53, no. 3 - 4, p. 365 - 390 (2002).

J. Wroblewski, "Ensembles of Classifiers Based on Approximate Reducts", Fundamenta Informaticae, vol. 47, no. 3 - 4, p. 351 - 360 (2001).

### See Also

`FS.quickreduct.RST` and `FS.feature.subset.computation`.

### Examples

    ###################################################
    ## Example 1: Evaluate reduct and generate
    ##            new decision table
    ###################################################
    data(RoughSetData)
    decision.table <- RoughSetData$hiring.dt

    ## evaluate single reduct
    res.1 <- FS.greedy.heuristic.superreduct.RST(decision.table, qualityF = X.nOfConflicts)
    print(res.1)

    ## generate new decision table according to the reduct
    new.decTable <- SF.applyDecTable(decision.table, res.1)

[Package RoughSets version 1.3-7 Index]