predict.RuleSetFRST: The predicting function for rule induction methods based on FRST

Description

It is a function used to obtain predicted values after obtaining rules by using rule induction methods. We have provided the functions RI.GFRS.FRST and RI.hybridFS.FRST to generate rules based on FRST.

Usage

# S3 method for RuleSetFRST
predict(object, newdata, ...)

Value

The predicted values.

Arguments

object: a "RuleSetFRST" class resulted by RI.GFRS.FRST and RI.hybridFS.FRST.
newdata: a "DecisionTable" class containing a data frame or matrix (m x n) of data for the prediction process, where m is the number of instances and n is the number of input attributes. It should be noted that this data must have colnames on each attributes.
...: the other parameters.

Author

Lala Septem Riza

Examples

Run this code

##############################################
## Example: Classification Task
##############################################
data(RoughSetData)
decision.table <- RoughSetData$pima7.dt

## using RI.hybrid.FRST for generating rules
control <- list(type.aggregation = c("t.tnorm", "lukasiewicz"),
           type.relation = c("tolerance", "eq.1"), t.implicator = "lukasiewicz")
rules.hybrid <- RI.hybridFS.FRST(decision.table, control)

## in this case, we are using the same data set as the training data
res.1 <- predict(rules.hybrid, decision.table[, -ncol(decision.table)])

## using RI.GFRS.FRST for generating rules
control <- list(alpha.precision = 0.01, type.aggregation = c("t.tnorm", "lukasiewicz"),
                type.relation = c("tolerance", "eq.3"), t.implicator = "lukasiewicz")
rules.gfrs <- RI.GFRS.FRST(decision.table, control)

## in this case, we are using the same data set as the training data
res.2 <- predict(rules.gfrs, decision.table[, -ncol(decision.table)])

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

## using RI.hybrid.FRST for generating rules
control <- list(type.aggregation = c("t.tnorm", "lukasiewicz"),
           type.relation = c("tolerance", "eq.1"), t.implicator = "lukasiewicz")
rules <- RI.hybridFS.FRST(decision.table, control)

## in this case, we are using the same data set as the training data
res.1 <- predict(rules, decision.table[, -ncol(decision.table)])