# summary.RuleSetFRST: The summary function of rules based on FRST In janusza/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

 1 2 ## 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.

Lala Septem Riza

## Examples

 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)

janusza/RoughSets documentation built on May 31, 2018, 11:11 a.m.