R: The summary function of rules based on FRST

summary.RuleSetFRST {RoughSets} | R Documentation

## The summary function of rules based on FRST

### Description

This function enables the output of a summary of the rule induction methods.

### Usage

    ## S3 method for class 'RuleSetFRST'
    summary(object, ...)

### Arguments

`object` | a `"RuleSetFRST"` object. See `RI.hybridFS.FRST` and `RI.GFRS.FRST`.
`...` | the other parameters.

### Value

a description that contains the following information:

- The type of the considered model.

- The type of the considered method.

- The type of the considered task.

- The type of similarity.

- The type of triangular norm.

- The names of attributes and their type (whether nominal or not).

- The interval of the data.

- the variance values of the data.

- The rules. Every rule constitutes two parts which are IF and THEN parts. For example, `"IF pres is around 90 and preg is around 8 THEN class is 2"`. See `RI.GFRS.FRST`.

### Author(s)

Lala Septem Riza

### Examples

    ###########################################################
    ## Example 1: Regression problem
    ###########################################################
    data(RoughSetData)
    decision.table <- RoughSetData$housing7.dt

    control <- list(type.aggregation = c("t.tnorm", "lukasiewicz"), type.relation =
                    c("tolerance", "eq.3"), t.implicator = "lukasiewicz")
    res.1 <- RI.hybridFS.FRST(decision.table, control)

    summary(res.1)
    ###########################################################
    ## Example 2: Classification problem
    ##############################################################
    data(RoughSetData)
    decision.table <- RoughSetData$pima7.dt

    control <- list(type.aggregation = c("t.tnorm", "lukasiewicz"), type.relation =
                    c("tolerance", "eq.3"), t.implicator = "lukasiewicz")
    res.2 <- RI.hybridFS.FRST(decision.table, control)

    summary(res.2)

[Package RoughSets version 1.3-7 Index]