CREA.RBS: An expert rule based system using Reduction Based on...

In MachineLearning: Machine Learning Algorithms for Innovation in Tourism

Description

CREA-RBS is a rule reduction method for allocating a significance value to each rule in the system so that experts may select the rules that should be considered as preferable and understand the exact degree of correlation between the different rule attributes.

Arguments

formula	a formula of the form y ~ x1 + x2 + ...
data	the data frame that contains the variables specified in formula.

Details

Significance is calculated from the antecedent frequency and rule frequency parameters for each rule; if the first one is above the minimal level and rule frequency is in a critical interval, its significance ratio is computed by the algorithm. These critical boundaries are calculated by an incremental method and the rule space is divided according to them. The significance function is defined for these intervals.

Value

A MLA object of subclass CREA-RBS

References

Almiñana, M., Escudero, L. F., Pérez-Martín, A., Rabasa, A., & Santamaría, L. (2014). A classification rule reduction algorithm based on significance domains. Top, 22(1), 397-418.

Examples

## Load a Dataset
data(EGATUR)
## Generate a CREA-RBS model, remmember only support discretized variables
CREA.RBS(GastoTotalD~pais+aloja+motivo,data=EGATUR)

MachineLearning documentation built on March 13, 2020, 2:54 a.m.