summary.RuleSetFRST: The summary function of rules based on FRST

In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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)

RoughSets documentation built on Dec. 16, 2019, 1:37 a.m.