R: Rule induction from indiscernibility classes.

RI.indiscernibilityBasedRules.RST {RoughSets} | R Documentation

## Rule induction from indiscernibility classes.

### Description

Rule induction from indiscernibility classes.

### Usage

    RI.indiscernibilityBasedRules.RST(decision.table, feature.set)

### Arguments

`decision.table` | an object inheriting from the `"DecisionTable"` class, which represents a decision system. See `SF.asDecisionTable`.
`feature.set` | an object inheriting from the `"FeatureSubset"` class which is a typical output of feature selection methods based on RST e.g. `FS.greedy.heuristic.reduct.RST`. See also `FS.reduct.computation`, `FS.feature.subset.computation` and `FS.all.reducts.computation` based on RST.

### Details

This function generates "if-then" decision rules from indiscernibility classes defined by a given subset of conditional attributes.

After obtaining the rules, decision classes of new objects can be predicted using the `predict` method or by a direct call to `predict.RuleSetRST`.

### Value

An object of a class `"RuleSetRST"`, which is a list with additional attributes:

- `uniqueCls`: a vector of possible decision classes,

- `supportDenominator`: an integer giving the number of objects in the data,

- `clsProbs`: a vector giving the a priori probability of the decision classes,

- `majorityCls`: a class label representing the majority class in the data,

- `method`: the type a rule induction method used for computations,

- `dec.attr`: a name of the decision attribute in the data,

- `colnames`: a vector of conditional attribute names.

Each rule is a list with the following elements:

- `idx`: a vector of indexes of attribute that are used in antecedent of a rule,

- `values`: a vector of values of attributes indicated by `idx`,

- `consequent`: a value of the consequent of a rule,

- `support`: a vactor of integers indicating objects from the data, which support a given rule,

- `laplace`: ia numeric value representing the Laplace estimate of the rule's confidence.

### Author(s)

Andrzej Janusz

### See Also

`predict.RuleSetRST`, `RI.CN2Rules.RST`, `RI.LEM2Rules.RST`, `RI.AQRules.RST`.

### Examples

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

    ## determine feature subset/reduct
    reduct <- FS.reduct.computation(hiring.data,
                                    method = "permutation.heuristic",
                                    permutation = FALSE)

    rules <- RI.indiscernibilityBasedRules.RST(hiring.data, reduct)
    rules

[Package RoughSets version 1.3-7 Index]