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

1 2 |

`object` |
a |

`...` |
the other parameters. |

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`

.

Lala Septem Riza

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
###########################################################
## 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)
``` |

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